METHODS, COMPOSITIONS, AND DEVICES UTILIZING MicroRNA TO DETERMINE PHYSIOLOGICAL CONDITIONS

ABSTRACT

Methods, compositions, and devices are disclosed which use microRNA to detect, predict, treat, and monitor physiological conditions such as disease or injury. microRNA are isolated and their differential expression is measured to provide diagnostic information. This information may then be utilized for evaluation and/or treatment purposes.

This application is a divisional of U.S. patent application Ser. No. 14/300,071, filed Jun. 9, 2014, which is a continuation of U.S. patent application Ser. No. 12/615,969, now U.S. Pat. No. 8,748,101, which claims priority to U.S. Provisional Patent Application Ser. No. 61/112,985, filed Nov. 10, 2008. The contents of these applications are hereby fully incorporated by reference in its entirety.

BACKGROUND

Disclosed herein are various methods, compositions, and devices utilizing microRNA, such as microRNA-based markers, to detect, predict, treat, or monitor various physiological or pathological conditions.

The ideal diagnostic marker has to fulfill certain key requirements including being specific, sensitive, robust, and non-invasive. Current disease diagnoses are primarily based on two different but complementary approaches—physical imaging and biomolecular profiling. Both approaches currently suffer from a lack of specificity and early detection capability. Tissue-specific blood biomarkers can increase the specificity to selected organs. However, the levels of these tissue-specific biomarkers are usually low in blood. In addition, the difficulty of developing suitable capture agents for proteins makes the identification and development of new molecular diagnostic markers difficult.

It would be desirable to provide new methods, compositions, and devices for diagnosing physiological and pathological conditions.

BRIEF DESCRIPTION

The present disclosure relates, in different embodiments, to the use of the levels of microRNA sequences (miRNA) in body fluids to establish correlations with the body's pathophysiological conditions. Exemplary body fluids include, but are not limited to, serum, plasma, saliva, urine, tears, amniotic fluid, sweat, cerebrospinal fluid, seminal fluid (semen), lung mucus (e.g. from bronchial lavage), pleural fluid, peritoneal fluid, colostrums, and breast milk. These levels can then provide diagnostic and/or predictive information with regard to important issues of health and disease.

Disclosed are methods of using microRNA sequences to detect a physiological condition. The methods comprise: isolating microRNA sequences from a biological sample; generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences; and detecting the physiological condition based on the identity or the levels of the differentially expressed microRNA sequences.

The biological sample may be a biopsy material, tissue, or body fluid. In embodiments, the biological sample comprises a body fluid selected from the group consisting of serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.

The microRNA sequences may be isolated by extracting the biological sample with an organic solvent to obtain an aqueous phase containing the microRNA sequences; and purifying the aqueous phase through a silica membrane to isolate the microRNA sequences.

The microRNA profile can be generated using hybridization to identify a microRNA sequences; or by using a quantitative polymerase chain reaction to identify the level of a microRNA sequences.

The reference can be a table of the levels of expressed microRNA sequences in a normal person, or a reference sample.

The biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.

The isolated microRNA sequences may be specific to a biological pathway, a cell type, or a tissue.

The physiological condition may be a disease, injury, or infection.

Also disclosed are methods of using microRNA sequences to detect or predict a physiological condition. These methods also comprise: generating a microRNA profile from a biological sample, the profile including the levels of expressed microRNA sequences in the biological sample; and comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. The physiological condition could then be detected or predicted based on the identity or the levels of the differentially expressed microRNA sequences. Alternatively, the physiological condition can be identified, and a treatment can then be administered based on the identity of the physiological condition.

Further disclosed are methods of using microRNA sequences to monitor a physiological condition, comprising: generating a first microRNA profile from a first biological sample of a patient; administering a treatment to the patient; generating a second microRNA profile from a second biological sample of the patient; comparing the second microRNA profile with the first microRNA profile to identify differentially expressed microRNA sequences; and identifying a change in the physiological condition based on the identity or the amounts of the differentially expressed microRNA sequences.

Additionally disclosed are methods of using microRNA sequences to treat a physiological condition. The methods comprise: identifying at least one microRNA sequence based on the physiological condition; and manipulating the level of the at least one microRNA sequence to treat the physiological condition. Manipulating the level of the at least one microRNA sequence may comprise: constructing a specific DNA or RNA sequence related to the at least one microRNA sequence; and delivering the specific DNA or RNA sequence to a targeted cell, tissue, or organ.

Also disclosed are methods of using microRNA sequences to detect, predict, or treat a physiological condition. The methods comprise: generating a microRNA profile from a biological sample; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting, predicting, or treating the physiological condition based on the identity or the levels of the at least one differentially expressed microRNA sequence. In alternative embodiments, at least two differentially expressed microRNA sequences are identified.

Other methods of detecting or predicting a physiological condition comprise generating a microRNA profile from a biological sample, wherein the microRNA profile comprises at least one specific microRNA sequence; and comparing the microRNA profile to a reference to provide information useful for detecting or predicting the physiological condition. In alternative embodiments, the microRNA profile comprises at least two specific microRNA sequences.

A differentially expressed microRNA sequence can be identified by comparing the amount of a particular microRNA sequence in the microRNA profile with the amount of that particular microRNA sequence in the reference. A differentially expressed microRNA sequence is identified when the ratio of the amount in the microRNA profile to the amount in the reference is at least 1.5, or at least 3.

When the physiological condition is related to liver disease or liver injury, in some embodiments, the microRNA profile or the specific microRNA sequence(s) may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-127, mmu-miR-130a, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-141, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-200c, mmu-miR-202-3p, mmu-miR-205, mmu-miR-22, mmu-miR-23b, mmu-miR-26a, mmu-miR-27b, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-29b, mmu-miR-30a, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-320, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-370, mmu-miR-375, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-494, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, mmu-miR-93, mmu-miR-99a, and human orthologs thereof.

In other embodiments where the physiological condition is related to liver disease or liver injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-202-3p, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-375, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, and human orthologs thereof.

In particular embodiments, the at least one differentially expressed microRNA sequence or the at least one specific sequence comprises hsa-miR-122. In more specific embodiments, they comprise hsa-miR-122 and either hsa-miR-486-3p or hsa-miR-486-5p (i.e. the human orthologs to mmu-miR-486). The ratio of the amount of miR-122 to the amount of miR-486 may be greater than 4.0, including greater than 6.0.

When the physiological condition is neurological disease or neurological injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-1, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-127, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-136, mmu-miR-138, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140, mmu-miR-140*, mmu-miR-142-3p, mmu-miR-143, mmu-miR-146a, mmu-miR-146b, mmu-miR-148b, mmu-miR-150, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-183, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-194, mmu-miR-19a, mmu-miR-200a, mmu-miR-200b, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-206, mmu-miR-208a, mmu-miR-21, mmu-miR-211, mmu-miR-221, mmu-miR-222, mmu-miR-223, mmu-miR-27a, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29b, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-30c, mmu-miR-31, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-451, mmu-miR-455, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-484, mmu-miR-486, mmu-miR-487b, mmu-miR-497, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-708, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-93, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.

In other embodiments where the physiological condition is neurological disease or neurological injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140*, mmu-miR-146a, mmu-miR-148b, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-19a, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-208a, mmu-miR-211, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.

When the physiological condition is related to lung disease or lung injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-10b, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-198, hsa-miR-22, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-494, hsa-miR-497, hsa-miR-498, hsa-miR-500, hsa-miR-503, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518b, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-520h, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551a, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-572, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-601, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-622, hsa-miR-623, hsa-miR-624*, hsa-miR-627, hsa-miR-633, hsa-miR-634, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-648, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-662, hsa-miR-663, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b, hsa-miR-92b*, hsa-miR-933, hsa-miR-934, hsa-miR-935, hsa-miR-936, hsa-miR-937, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.

In other embodiments where the physiological condition is related to lung disease or lung injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-500, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-623, hsa-miR-624*, hsa-miR-633, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-768-3p, hsa-miR-768-5p, hsa-miR-801, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b*, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.

The physiological condition may also be a lung disease or lung injury, such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), also known as interstitial lung disease (ILD).

In embodiments, the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-448, hsa-miR-451, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-92a-2*, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof.

In other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.

In yet other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.

In some alternate embodiments, the biological sample is plasma and at least two differentially expressed microRNA sequences or specific microRNA sequences are identified. At least one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The other one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-451, hsa-miR-448, hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.

In yet other embodiments, the biological sample is lung tissue and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-923, hsa-miR-1225-5p, hsa-miR-22, hsa-miR-223, hsa-miR-638, kshv-miR-K12-3, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences.

In still other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-940, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.

Also disclosed are methods of using microRNA sequences to detect a lung condition, comprising: generating a microRNA profile from a biological sample; and detecting the lung condition based on the levels of at least one overexpressed microRNA sequence and at least one underexpressed microRNA sequence. The at least one overexpressed microRNA sequence is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The at least one underexpressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-448, and hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile examines only a selection of these listed microRNA sequences.

Also disclosed are methods of detecting or predicting certain physiological conditions in a patient. Those methods comprise generating a microRNA profile from a biological sample provided by the patient; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting or predicting the physiological condition based on the identity or the amounts of the at least one differentially expressed microRNA sequence. The biological sample comprises (i) serum or plasma; and (ii) an additional body fluid specific to a particular location of the body that is relevant to the particular physiological condition. In a first embodiment, the biological sample further comprises amniotic fluid and the physiological condition is the health status of a fetus being carried by the patient. In a second embodiment, the biological sample further comprises urine and the physiological condition is the health status of a bladder or a kidney of the patient. In a third embodiment, the biological sample further comprises breast milk and the physiological condition is the health status of a breast of the patient. In a fourth embodiment, the biological sample further comprises saliva and the physiological condition is the health status of the head and neck region of the patient. In a fifth embodiment, the biological sample further comprises tears and the physiological condition is the health status of an eye of the patient. In a sixth embodiment, the biological sample further comprises semen and the physiological condition is the health status of a prostate or male reproductive organ of the patient. In a seventh embodiment, the biological sample further comprises synovial fluid and the physiological condition is the health status of a joint of the patient. In an eighth embodiment, the biological sample further comprises sweat and the physiological condition is the health status of the skin of the patient. In a ninth embodiment, the biological sample further comprises cerebrospinal fluid and the physiological condition is the health status of the central nerve system of the patient.

Also disclosed are methods of diagnosing a physiological condition. The methods comprise taking a sample of a body fluid and a sample of a body tissue from a patient. A first microRNA profile is generated from the body fluid sample, and a second microRNA profile is generated from the body tissue sample. At least two differentially expressed microRNA sequences are identified in the first microRNA profile by comparing the first microRNA profile to a first reference. At least two differentially expressed microRNA sequences are identified in the second microRNA profile by comparing the second microRNA profile to a second reference. The physiological condition is then diagnosed based on the differentially expressed microRNA sequences identified. In particular, the differentially expressed microRNA sequences in the first microRNA profile are different from the differentially expressed microRNA sequences in the second microRNA profile. This difference in the differentially expressed microRNA sequences between the body fluid and the body tissue increases the probability of a correct diagnosis.

Also included are assays for detecting the identity and/or levels of the various combinations of microRNA sequences described above.

These and other non-limiting aspects and/or objects of the disclosure are more particularly described below.

BRIEF DESCRIPTION OF THE DRAWINGS

The following is a brief description of the drawings, which are presented for the purposes of illustrating the disclosure set forth herein and not for the purposes of limiting the same.

FIGS. 1A-1B are electropherograms of RNA.

FIG. 2 is a microRNA profile showing changes in specific microRNA expression levels over time in the liver after exposing the animal to a high dose of acetaminophen.

For reference, the text on the right-hand side of FIG. 2 reads, in order from top to bottom: mmu-miR-720, mmu-miR-1224, mmu-miR-122, mmu-miR-494, mmu-miR-609, mmu-miR-21, mmu-miR-22, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-574-5p, mmu-let-7a, mmu-let-7f, mmu-miR-192, mmu-miR-194, mmu-miR-212, mmu-let-7g, mmu-miR-29b, mmu-miR-26a, mmu-miR-30c, mmu-miR-29a, mmu-miR-188-5p, mmu-miR-709, mmu-miR-466g, mmu-miR-574-3p, mmu-miR-125a-3p, mmu-miR-125b-5p, mmu-miR-29c, mmu-miR-483, mmu-miR-600, mmu-miR-705, mmu-miR-721, mmu-miR-376b, mmu-miR-706, mmu-miR-710, mmu-miR-711, mmu-let-7c-2*, mmu-miR-376a, mmu-miR-891, mmu-miR-452, mmu-miR-467a*, mmu-miR-718, mmu-miR-500, mmu-miR-669c, mmu-miR-714, mmu-miR-290-5p, mmu-miR-134, mmu-miR-27b, mmu-miR-671-5p, mmu-miR-135a*, and mmu-miR-877*.

FIG. 3 is a microRNA profile showing differences in specific microRNA levels between plasma samples from a treated group and a control group.

For reference, the text on the right-hand side of FIG. 3 reads, in order from top to bottom: mmu-miR-21, mmu-miR-122, mmu-miR-22, mmu-miR-192, mmu-miR-29a, mmu-miR-30a, mmu-miR-130a, mmu-miR-29c, mmu-miR-30a, mmu-miR-148a, mmu-miR-19b, mmu-miR-101b, mmu-miR-15a, mmu-miR-685, mmu-let-7g, mmu-miR-27b, mmu-miR-574-5p, mmu-miR-671-5p, mmu-miR-107, mmu-let-7d*, mmu-miR-29b, mmu-miR-193, mmu-miR-194, mmu-miR-101a, mmu-miR-185, mmu-miR-221, mmu-miR-294*, mmu-miR-877, mmu-miR-291a-5p, mmu-miR-877*, mmu-miR-339-3p, mmu-miR-466f-3p, mmu-miR-30c-1*, mmu-miR-199b, mmu-miR-199a-5p, mmu-miR-193b, mmu-miR-370, mmu-miR-882, mmu-miR-327, mmu-miR-127, mmu-miR-714, mmu-miR-150, mmu-miR-125a-5p, mmu-miR-141, mmu-miR-23b, mmu-miR-145, mmu-miR-320, mmu-miR-342-3p, mmu-miR-200c, mmu-miR-223, mmu-miR-99a, mmu-miR-202-3p, mmu-miR-494, mmu-miR-652, mmu-miR-375, mmu-miR-125a-3p, mmu-miR-124, mmu-miR-721, mmu-miR-93, mmu-miR-483, mmu-miR-205, mmu-miR-712, mmu-miR-26a, mmu-miR-710, mmu-miR-23a, mmu-miR-135a*, mmu-miR-711, mmu-miR-720, mmu-miR-125b-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-451, mmu-miR-486, and mmu-miR-1224.

FIG. 4 is a graph of intensities for two selected microRNA sequences, mir-122 and mir-486 in plasma after exposing the animal to different doses of acetaminophen.

FIG. 5 is a graph of the ratio between mir-122 and mir-486 (either median or average intensities) for the same data as FIG. 4.

FIG. 6 is a microRNA profile showing differences in microRNA expression levels between normal brain tissue and diseased brain tissue.

FIG. 7 is a microRNA profile showing differences in microRNA expression levels as a disease progressed in lung tissue.

FIG. 8 is a microRNA profile showing differences in microRNA expression levels between serum and urine samples.

FIG. 9 is a graph comparing miRNA expression levels in control plasma samples with ILD plasma samples.

FIGS. 10A-10B are graphs showing the signal strength in the ILD and control plasma samples of FIG. 9.

FIG. 11 is a graph showing the signal strength for all oligonucleotide probes used to target certain microRNA sequences.

FIG. 12 is a graph showing the difference in the signal strength for certain microRNA sequences in the ILD and control plasma samples of FIG. 9.

FIG. 13 is a graph showing the degree of overexpression in certain microRNA sequences in the ILD and control plasma samples of FIG. 9.

FIG. 14 is a graph comparing miRNA expression levels in ILD tissue samples with ILD plasma samples.

FIG. 15 is a graph comparing miRNA expression levels in control lung tissue samples with ILD lung tissue samples.

FIG. 16 is a graph showing the effect of normalization on data in a data analysis method.

FIGS. 17A-17B are graphs showing the effect of normalization on the quality of data.

FIG. 18 is a graph clustering normalized miRNA data.

FIG. 19 is a graph showing the p-value distribution of all miRNA in a sample.

FIG. 20 is a collection of charts showing the selection of panels that separates data.

DETAILED DESCRIPTION

A more complete understanding of the processes and apparatuses disclosed herein can be obtained by reference to the accompanying drawings. These figures are merely schematic representations based on convenience and the ease of demonstrating the existing art and/or the present development, and are, therefore, not intended to indicate relative size and dimensions of the assemblies or components thereof.

Although specific terms are used in the following description for the sake of clarity, these terms are intended to refer only to the particular structure of the embodiments selected for illustration in the drawings, and are not intended to define or limit the scope of the disclosure. In the drawings and the following description below, it is to be understood that like numeric designations refer to components of like function.

MicroRNAs (also known as miRNA) are small but potent regulatory non-coding ribonucleic acid (RNA) sequences first identified in C. elegans in 1993. miRNA may be about 21 to about 23 nucleotides in length. Through sequence complementation, microRNA interacts with messenger RNA (mRNA) and affects the stability of mRNA and/or the initiation and progression of protein translation. It has been estimated that over 30% of the mRNAs are regulated by microRNA. Like mRNA, some of the microRNAs also display restricted tissue distribution. The biological function of microRNA is yet to be fully understood; however, it has been shown that microRNA sequences are involved in various physiological and pathological conditions, including differentiation, development, cancer, and neurological disorders. Unlike mRNA and proteins, microRNA is reasonably well conserved across different species. Thus, a specific microRNA sequences which is shown to correlate to a particular condition, such as disease or injury, in one species, should also correlate to that particular condition in other species, particularly humans (i.e. Homo sapiens). This correlation provides useful diagnostic content.

MicroRNAs can also be manipulated with commonly used molecular biology techniques including complementary DNA (cDNA) synthesis, polymerase chain reactions, Northern blotting, and array based hybridization. This makes it possible to easily investigate the function(s) of a given microRNA sequences of interest.

A microRNA is encoded by a gene. When the DNA of the gene is transcribed into RNA, the RNA is not subsequently translated into protein. Instead each primary transcript (a pri-mir) is processed into a short stem-loop structure (a pre-mir) and finally into a mature sequence, designated miR. The primary transcript can form local hairpin structures, which ordinarily are processed such that a single microRNA sequence accumulates from one arm of a hairpin precursor molecule. Sometimes the primary transcript contains multiple hairpins, and different hairpins give rise to different microRNA sequences.

The microRNA sequences discussed herein are named according the miRBase database available at http://microrna.sanger.ac.uk/ and maintained by the Wellcome Trust Sanger Institute (now redirected to http://www.miRBase.org/). Generally speaking, microRNA sequences are assigned sequential numerical identifiers, with the numerical identifier based on sequence similarity. A 3- or 4-letter prefix designates the species from which the microRNA sequence came. For example, the hsa in hsa-miR-101 refers to homo sapiens.

Orthologous sequences, or orthologs, refer to microRNA sequences that are in different species but are similar (i.e. homologous) because they originated from a common ancestor. Generally speaking, orthologs have the same numerical identifier and are believed to serve a similar function. For example, mmu-miR-101 and hsa-miR-101 are in mouse and human, respectively, and are orthologs to each other. In this disclosure, microRNA sequences are referred to without the prefix designating the species, and should be construed as preferentially referring to the human microRNA sequence and the murine sequence. For example, miR-101 should be construed as referring to hsa-miR-101 and mmu-miR-101.

Paralogous sequences, or paralogs, are microRNA sequences that differ from each other in only a few positions. Paralogs occur within a species. Paralogs are designated with letter suffixes. For example, mmu-miR-133a and mmu-miR-133b are paralogs.

Identical microRNA sequences that originate from separate genomic loci are given numerical suffixes, such as hsa-miR-26a-1 and hsa-miR-26a-2.

Sometimes, two different mature microRNA sequences are excised from opposite arms of the same hairpin precursor. The two microRNA sequences can be designated in at least two ways. First, when it is possible to determine which arm gives rise to the predominantly expressed miRNA sequence, an asterisk has been used to denote the less predominant form, such as hsa-let-7b and hsa-let-7b*. Alternatively, they are named to designate whether they come from the 5′ or 3′ arm, such as hsa-miR-125a-3p and hsa-miR-125a-5p.

Specific microRNA sequences have been identified in the blood that are associated with liver injuries. Thus, the levels of selected microRNA sequences can be used to detect, predict, or diagnose diseases, predict and monitor therapeutic responses, and/or predict disease outcomes.

MicroRNA-based blood markers offer superior properties over existing markers. Such markers are sensitive, in part because microRNA signals can be amplified using standard polymerase chain reactions (PCR) while protein-based markers cannot be easily amplified. Because the sequence and expression profile of microRNAs are largely conserved across species, discoveries made in animal models can be easily translated to and adapted for use in humans. MicroRNA assays can be quickly performed and developed with standard PCR or array based systems; therefore, beside PCR primers, there is no need to develop special detection agents. Finally, since microRNA can be easily accessed in various body fluids, obtaining such diagnostic information can be done non-invasively.

The level of specific microRNA sequences(s) in a cell, tissue, or body fluid(s) can be used to monitor the physiopathological conditions of the body.

Sets of microRNA sequences in the tissue and the serum have been identified that are associated with liver injuries, lung injuries, and lung diseases. The combination of information from multiple microRNA expression level changes can further enhance the sensitivity and specificity of disease/injury detection, including using the ratio of paired microRNA sequences.

MicroRNA profiles, for example a microRNA profile of tissue-specific microRNA sequences, could be used to monitor the health status of that tissue. Those microRNA sequences could also be used as therapeutic targets for diseases associated with the tissue.

MicroRNA sequences from microbes or infectious agents, such as bacteria and viruses, could be used as an indication of infection. Host responses could be monitored by using the combination of microRNA sequences from infectious agents and the host as measured from the host's body fluids.

Biological processes occurring in a number of cell types or tissues could be monitored by the use of microRNA profiles specific to a process or network. These specific microRNA sequences could also be used as therapeutic targets for diseases associated with the biological processes.

The methods of the present disclosure could be used to detect, predict, monitor, or treat a physiological condition such as a disease, injury, or infection. Generally, the methods include: (a) isolating microRNA sequences from a biological sample; (b) generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; and (c) comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. Based on the identity or the levels of the differentially expressed microRNA sequences, the physiological condition could be detected, predicted, or monitored; or a treatment could be indicated, administered, or monitored accordingly.

The biological sample is generally non-invasive, and may be, for example, a biopsy material, tissue, or body fluid. Exemplary body fluids include serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.

Combinations of different biological samples are also contemplated for providing more specific diagnoses. For example, plasma and serum would provide some general indicators of health, while a specific body fluid could be included for specific information. For example, if one wanted to assess the health status of a fetus being carried by the mother, one might test the amniotic fluid along with the mother's plasma or serum. As another example, one might test the urine to assess the health status of a bladder or a kidney. Testing the breast milk would help assess the health status of a breast of the patient providing the biological sample. Testing the saliva would help assess the health status of the head and neck region. Testing the tears would help assess the health status of an eye of the patient providing the biological sample. Testing semen would help assess the health status of a prostate or male reproductive organ. Testing the synovial fluid would help assess the health status of a joint of the patient providing the biological sample. Testing the sweat would help assess the health status of the skin. Testing the cerebrospinal fluid would help assess the health status of the central nerve system. The term “health status” refers only to the physiological condition of the given body part, and has no specific meaning otherwise.

Isolating microRNA can be done by various methods. For example, the biological sample may be extracted with an organic solvent to obtain an aqueous phase containing the microRNA sequences. The aqueous phase is then purified through a silica membrane to isolate the microRNA sequences.

A microRNA profile can then be generated from the isolated microRNA sequences. Generally speaking, the microRNA profile provides the identity of specific microRNA sequences and/or the expression level (i.e. amount) of each specific microRNA sequence. An exemplary microRNA profile is seen in FIG. 2, which shows the expression levels for several microRNA sequences from several different liver samples that have been exposed to a high dose of acetaminophen. The microRNA profile of FIG. 2 has six columns, but a microRNA profile may be simply one column (along with the identifying microRNA). The expression level can be displayed either as a sliding color scale or simply as numerical values. The microRNA profile can be generated by using hybridization to identify the microRNA sequences and/or using quantitative PCR (qPCR) to identify the levels of one or more particular microRNA sequences. It should be noted that the diagnostic information may be in the identity of the microRNA sequences themselves, or in the absolute or relative levels of the microRNA sequence, either between two microRNA sequences in a given sample or between two samples for a given microRNA sequence. A reference table could be provided, for example from a reference sample taken from the patient or from a table of levels of expressed microRNA sequences in a normal (healthy) person or a table compiled from the expressed microRNA sequences over a large sample of people. Differentially expressed microRNA sequences can then be identified by comparing the microRNA profile of the biological sample with the reference sample or table to obtain diagnostic information. The term “differentially expressed” refers only to the fact that the amount or expression level has changed. The direction of change (i.e. upwards or downwards, overexpressed or underexpressed) is not significant, except as otherwise stated.

In particular embodiments, it is contemplated that identifying at least one specific microRNA sequence as being differentially expressed would be sufficient to identify a particular physiological condition as occurring. In other embodiments, at least two differentially expressed microRNA sequences are identified. This provides for an additional degree of confirmation in the identity of the physiological condition.

In using the terms “generating” and “identifying,” it is contemplated that these actions may be performed directly or indirectly. For example, a laboratory technician may perform the actions that directly “generate” a microRNA profile. The physician who ordered the microRNA profile that was directly “generated” by the laboratory technician may be considered to have indirectly “generated” the microRNA profile.

Because microRNA sequences and expression levels are generally conserved across species, it is contemplated that sequences and levels from other species would contain useful diagnostic information. For example, the biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.

It has been found that microRNA sequences and their expression levels can differ depending on their location in the body. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.

Table 1 lists some microRNA sequences which have been found to be specific to certain tissues in the human body.

TABLE 1 Human tissue Human tissue Tissue specific miRNA Tissue specific miRNA Adipose hsa-miR-452 Placenta hsa-miR-527 Adipose hsa-miR-196a Placenta hsa-miR-377 Adipose hsa-miR-224 Placenta hsa-miR-526c Adipose hsa-miR-335 Placenta hsa-miR-524* Adipose hsa-miR-452* Placenta hsa-miR-517* Adipose hsa-miR-432* Placenta hsa-miR-450 Adrenal hsa-miR-409-5p Placenta hsa-miR-503 Adrenal hsa-miR-494 Placenta hsa-miR-526b* Adrenal hsa-miR-485-5p Placenta hsa-miR-371 Adrenal hsa-miR-360-5p Placenta hsa-miR-519b Adrenal hsa-miR-154 Placenta hsa-miR-516-3p Adrenal hsa-miR-370 Placenta hsa-miR-526a Adrenal hsa-miR-381 Placenta hsa-miR-523 Adrenal hsa-miR-369 Placenta hsa-miR-518a-2* Adrenal hsa-miR-485-3p Placenta hsa-miR-518c* Adrenal hsa-miR-134 Placenta hsa-miR-520b Adrenal hsa-miR-323 Placenta hsa-miR-518d Adrenal hsa-miR-7N Placenta hsa-miR-524 Adrenal hsa-miR-382 Placenta hsa-miR-519a Adrenal hsa-miR-7 Placenta hsa-miR-520a Adrenal hsa-miR-405 Placenta hsa-miR-521 Adrenal hsa-miR-127 Placenta hsa-miR-522 Adrenal hsa-miR-493 Placenta hsa-miR-520d Adrenal hsa-miR-379 Placenta hsa-miR-525 Adrenal hsa-miR-432 Placenta hsa-miR-512-5p Adrenal hsa-miR-299 Placenta hsa-miR-520a* Adrenal hsa-miR-433 Placenta hsa-miR-519a* Adrenal hsa-miR-376a Placenta hsa-miR-517a Adrenal hsa-miR-202* Placenta hsa-miR-517b Adrenal hsa-miR-137 Placenta hsa-miR-515-5p Adrenal hsa-miR-501 Placenta hsa-miR-525* Adrenal hsa-miR-202 Placenta hsa-miR-518 Adrenal hsa-miR-491 Placenta hsa-miR-512-3p Bladder hsa-miR-451 Placenta hsa-miR-517c Brain hsa-miR-330 Placenta hsa-miR-518a Brain hsa-miR-219 Placenta hsa-miR-519d Brain hsa-miR-124 Placenta hsa-miR-518c Brain hsa-miR-9 Placenta hsa-miR-518e Brain hsa-miR-9* Placenta hsa-miR-520g Brain hsa-miR-124a Placenta hsa-miR-519c Brain hsa-miR-129 Placenta hsa-miR-515-3p Brain hsa-miR-124b Placenta hsa-miR-520b Brain hsa-miR-137 Placenta hsa-miR-372 Brain hsa-miR-383 Placenta hsa-miR-520a Brain hsa-miR-433 Placenta hsa-miR-520c Brain hsa-miR-348 Placenta hsa-miR-373 Brain hsa-miR-323 Placenta hsa-miR-520b Brain hsa-miR-153 Placenta hsa-miR-154* Brain hsa-miR-128b Placenta hsa-miR-520c Brain hsa-miR-128a Placenta hsa-miR-493 Brain hsa-miR-485-5p Placenta hsa-miR-381 Brain hsa-miR-370 Placenta hsa-miR-151 Brain hsa-miR-485-3p Placenta hsa-miR-495 Brain hsa-miR-181b Placenta hsa-miR-474 Brain hsa-miR-338 Placenta hsa-miR-369-5p Brain hsa-miR-154* Placenta hsa-miR-184 Brain hsa-miR-149 Placenta hsa-miR-489 Brain hsa-miR-213 Placenta hsa-miR-376a Brain hsa-miR-340 Placenta hsa-miR-500 Brain hsa-miR-181bN Placenta hsa-miR-369 Brain hsa-miR-181d Placenta hsa-miR-135b Brain hsa-miR-491 Placenta hsa-miR-432 Brain hsa-miR-184 Placenta hsa-miR-27aN Brain hsa-miR-138 Placenta hsa-miR-198 Brain hsa-miR-132 Placenta hsa-miR-224 Brain hsa-miR-181c Placenta hsa-miR-452* Brain hsa-miR-204 Placenta hsa-miR-433 Brain hsa-miR-328 Placenta hsa-miR-193b Brain hsa-miR-181a Placenta hsa-miR-494 Brain hsa-miR-432 Placenta hsa-miR-502 Brain hsa-miR-379 Placenta hsa-miR-335 Brain hsa-miR-324-5p Placenta hsa-miR-299 Brain hsa-miR-122 Placenta hsa-miR-149 Brain hsa-miR-134 Placenta hsa-miR-213 Brain hsa-miR-342 Placenta hsa-miR-30d Breast hsa-miR-452 Placenta hsa-miR-141 Breast hsa-miR-205 Placenta hsa-miR-301 Breast hsa-miR-489 Placenta hsa-miR-485-3p Colon hsa-miR-490 Placenta hsa-miR-141N Colon hsa-miR-363 Placenta hsa-miR-379 Colon hsa-miR-338 Placenta hsa-miR-130a Colon hsa-miR-31 Placenta hsa-miR-382 Colon hsa-miR-215 Placenta hsa-miR-99b Colon hsa-miR-200a* Placenta hsa-miR-370 Colon hsa-miR-200a Placenta hsa-miR-130b Colon hsa-miR-196b Placenta hsa-miR-27a Colon hsa-miR-196a Placenta hsa-miR-200cN Colon hsa-miR-194 Placenta hsa-miR-24 Colon hsa-miR-192 Placenta hsa-miR-30a-5p Colon hsa-miR-141N Placenta hsa-miR-30bN Colon hsa-miR-141 Placenta hsa-miR-221 Small hsa-miR-490 Placenta hsa-miR-200c Intestine Small hsa-miR-451 Placenta hsa-miR-320 Intestine Small hsa-miR-429 Placenta hsa-miR-127 Intestine Small hsa-miR-31 Placenta hsa-miR-485-5p Intestine Small hsa-miR-215 Placenta hsa-miR-30b Intestine Small hsa-miR-200bN Placenta hsa-miR-90a-3p Intestine Small hsa-miR-200b Placenta hsa-miR-181a Intestine Small hsa-miR-200a* Placenta hsa-miR-222 Intestine Small hsa-miR-198 Placenta hsa-miR-362 Intestine Small hsa-miR-194 Placenta hsa-miR-125a Intestine Small hsa-miR-192 Placenta hsa-miR-323 Intestine Small hsa-miR-138 Placenta hsa-miR-451 Intestine Cervix hsa-miR-196b Placenta hsa-miR-409-5p Cervix hsa-miR-99a Placenta hsa-miR-452 Heart hsa-miR-1 Placenta hsa-miR-518b Heart hsa-miR-107 Placenta hsa-miR-515-5p Heart hsa-miR-133a Placenta hsa-miR-130aN Heart hsa-miR-189 Skeletal hsa-miR-206 Muscle Heart hsa-miR-221 Skeletal hsa-miR-95 Muscle Heart hsa-miR-23bN Skeletal hsa-miR-133b Muscle Heart hsa-miR-302a Skeletal hsa-miR-133a Muscle Heart hsa-miR-302b Skeletal hsa-miR-128b Muscle Heart hsa-miR-302c Skeletal hsa-miR-1 Muscle Heart hsa-miR-302d Skeletal hsa-miR-489 Muscle Heart hsa-miR-300-3p Skeletal hsa-miR-378 Muscle Heart hsa-miR-367 Skeletal hsa-miR-422a Muscle Heart hsa-miR-378 Skeletal hsa-miR-128a Muscle Heart hsa-miR-422a Skeletal hsa-miR-196a Muscle Heart hsa-miR-422b Skeletal hsa-miR-502 Muscle Heart hsa-miR-452 Spleen hsa-miR-223 Heart hsa-miR-490 Spleen hsa-miR-139 Heart hsa-miR-491 Lymph Node hsa-miR-150 Heart hsa-miR-409 Lymph Node hsa-miR-142-3p Heart hsa-miR-7a Lymph Node hsa-miR-146b Pericardium hsa-miR-188 Lymph Node hsa-miR-146 Pericardium hsa-miR-369 Lymph Node hsa-miR-155 Pericardium hsa-miR-305 Lymph Node hsa-miR-363 Pericardium hsa-miR-452 PBMC hsa-miR-128a Pericardium hsa-miR-224 PBMC hsa-miR-124b Pericardium hsa-miR-511 PBMC hsa-miR-124a Pericardium hsa-miR-199b PBMC hsa-miR-137 Kidney hsa-miR-500 PBMC hsa-miR-431 Kidney hsa-miR-204 PBMC hsa-miR-129 Kidney hsa-miR-480 PBMC hsa-miR-128b Kidney hsa-miR-190 PBMC hsa-miR-138 Kidney hsa-miR-501 Thymus hsa-miR-183 Kidney hsa-miR-196a Thymus hsa-miR-96 Kidney hsa-miR-211 Thymus hsa-miR-128b Kidney hsa-miR-363 Thymus hsa-miR-213 Kidney hsa-miR-502 Thymus hsa-miR-205 Kidney hsa-miR-184 Thymus hsa-miR-128a Liver hsa-miR-122a Thymus hsa-miR-181bN Liver hsa-miR-30a-3p Thymus hsa-miR-182 Lung hsa-miR-223 Thymus hsa-miR-181b Esophagus hsa-miR-203 Thymus hsa-miR-181d Esophagus hsa-miR-205 Thymus hsa-miR-181a Esophagus hsa-miR-145 Thymus hsa-miR-181c Esophagus hsa-miR-210N Thymus hsa-miR-20b Esophagus hsa-miR-143 Thymus hsa-miR-383 Esophagus hsa-miR-31 Thymus hsa-miR-17-5p Esophagus hsa-miR-187 Thymus hsa-miR-142-3p Trachea hsa-miR-34b Stomach hsa-miR-211 Trachea hsa-miR-205 Stomach hsa-miR-188 Trachea hsa-miR-34cN Stomach hsa-miR-346 Trachea hsa-miR-34c Stomach hsa-miR-200a* Prostate hsa-miR-363 Stomach hsa-miR-375 Prostate hsa-miR-205 Stomach hsa-miR-148a Prostate hsa-miR-196b Stomach hsa-miR-200a Ovary hsa-miR-502 Stomach hsa-miR-200b Ovary hsa-miR-383 Stomach hsa-miR-200c Fallopian hsa-miR-34bN Stomach hsa-miR-200bN Tube Fallopian hsa-miR-34b Stomach hsa-miR-212 Tube Fallopian hsa-mi-34cN Stomach hsa-miR-31 Tube Fallopian hsa-miR-449 Stomach hsa-miR-7 Tube Fallopian hsa-miR-34c Stomach hsa-miR-153 Tube Fallopian hsa-miR-135a Stomach hsa-miR-429 Tube Pancreas hsa-miR-217 Stomach hsa-miR-107 Pancreas hsa-miR-216 Stomach hsa-miR-200cN Pancreas hsa-miR-375 Stomach hsa-miR-502 Pancreas hsa-miR-98 Stomach hsa-miR-203 Pancreas hsa-miR-163 Testicle hsa-miR-202 Pancreas hsa-miR-141N Testicle hsa-miR-506 Pancreas hsa-miR-148a Testicle hsa-miR-507 Pancreas hsa-miR-141 Testicle hsa-miR-510 Pancreas hsa-miR-7N Testicle hsa-miR-514 Pancreas hsa-miR-494 Testicle hsa-miR-513 Pancreas hsa-miR-130b Testicle hsa-miR-508 Pancreas hsa-miR-200cN Testicle hsa-miR-509 Pancreas hsa-miR-148b Testicle hsa-miR-202* Pancreas hsa-miR-182 Testicle hsa-miR-449 Pancreas hsa-miR-200a Testicle hsa-miR-34c Thyroid hsa-miR-138 Testicle hsa-miR-432* Thyroid hsa-miR-135a Testicle hsa-miR-184 Thyroid hsa-miR-206 Testicle hsa-miR-520c Thyroid hsa-miR-95 Testicle hsa-miR-520l Thyroid hsa-miR-1 Testicle hsa-miR-34cN Thyroid hsa-miR-7 Testicle hsa-miR-34b Uterus hsa-miR-10b Testicle hsa-miR-520b Uterus hsa-miR-196b Testicle hsa-miR-135b Uterus hsa-miR-502 Testicle hsa-miR-383 Testicle hsa-miR-204 Testicle hsa-miR-34bN

It has been found that microRNA sequences and their expression levels can differ depending on their location in different types of body fluid samples. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.

Table 2 lists some microRNA sequences which have been found to be highly abundant in different body fluids. The sequences in bold font are unique to the listed body fluid.

TABLE 2 Tears Urine Breast Milk Seminal Fluid Saliva Amniotic Fluid miR-518e miR-515-3p miR-518e miR-518e miR-335* miR-518e miR-335* miR-335* miR-26a-2* miR-590-3p miR-515-3p miR-335* miR-137 miR-892a miR-335* miR-588 miR-545* miR-302c miR-515-3p miR-509-5p miR-490-5p miR-873 miR-492 miR-515-3p miR-509-5p miR-223* miR-181d miR-590-5p miR-892a miR-452 miR-873 miR-302d miR-26a-1* miR-137 miR-518e miR-892a miR-223* miR-873 miR-137 miR-197 miR-27a miR-671-5p miR-892a miR-923 miR-524-5p miR-515-5p miR-923 miR-515-5p miR-590-3p miR-616* miR-509-5p miR-515-3p miR-509-5p miR-590-3p miR-302d miR-483-5p miR-513c miR-218 miR-873 miR-593* miR-616* miR-134 miR-595 miR-20b miR-483-5p miR-873 miR-590-5p miR-589 miR-515-3p miR-410 miR-616* miR-137 miR-101* miR-556-3p miR-515-5p miR-335* miR-580 miR-410 miR-130a miR-101* miR-598 miR-617 miR-609 miR-548d-5p miR-410 miR-138 miR-130a miR-671-5p miR-302d miR-223* miR-195 miR-652 miR-181b miR-524-5p miR-25* miR-590-5p miR-675 miR-325 miR-671-5p miR-892a miR-134 miR-616* miR-325 let-7i miR-892a miR-181d miR-92b miR-302d miR-134 miR-377* miR-578 miR-545* miR-598 miR-509-5p miR-29b miR-545* miR-580 miR-1 let-7a miR-210 Bronchial Lavage CSF Pleural Fluid Peritoneal Fluid Colostrum Plasma miR-515-3p miR-515-3p miR-515-3p miR-892a miR-509-5p miR-335* miR-335* miR-335* miR-892a miR-518e miR-181d miR-325 miR-509-5p miR-892a miR-509-5p miR-515-3p miR-335* miR-377* miR-483-5p miR-223* miR-134 miR-134 miR-518e miR-586 miR-892a miR-873 miR-590-5p miR-509-5p miR-515-5p miR-518e miR-223* miR-509-5p miR-515-5p miR-223* miR-223* let-7i miR-873 miR-302d miR-873 miR-515-5p miR-671-5p miR-539 miR-1225-3p miR-616* miR-335* miR-616* miR-873 miR-616* miR-302d miR-134 miR-920 miR-137 miR-483-5p miR-302d miR-545* miR-483-5p miR-616* miR-873 miR-186 miR-589 miR-324-3p miR-325 miR-302d miR-483-5p miR-515-3p miR-556-3p miR-616* miR-151-5p miR-518e miR-518c miR-616* miR-151-3p miR-92b miR-589 miR-923 miR-92b miR-134 miR-548b-3p miR-25* miR-377* miR-589 miR-923 miR-892a miR-192 miR-539 miR-923 miR-377* miR-302d miR-590-5p miR-151-5p miR-923 miR-652 miR-410 miR-374a miR-590-3p miR-598 miR-192 miR-518e miR-137 miR-598 miR-425 miR-187 miR-134 miR-556-3p miR-671-5p miR-937 miR-454 miR-873 miR-371-3p miR-767-3p miR-151-5p miR-335* miR-101* miR-218 miR-580 miR-505 miR-223* miR-885-5p miR-132 miR-923

With the diagnostic information obtained, a physiological condition could be detected, identified, predicted, treated, and/or monitored. For example, a treatment could be administered based on the identity of the physiological condition. A particular treatment could be monitored by taking a first sample, administering the treatment, taking a second sample, and comparing the microRNA profiles of the two samples to identify and/or track changes resulting from the treatment. Those changes could include the amounts of a particular microRNA sequence, or the identity of the differentially expressed microRNA sequences that have changed between the two samples.

It is also contemplated that manipulating the levels of microRNA sequences might itself be a treatment for a physiological condition. The microRNA level could be altered by constructing a specific DNA or RNA sequence related to the microRNA sequences, then delivering that DNA or RNA sequence to a targeted cell, tissue, or organ expressing the targeted microRNA sequences.

As discussed below, specific microRNA sequences are identified that may be useful in diagnosing and/or treating liver disease or injury, lung disease or injury, and neurological disease or injury. Such conditions include chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) (also known as interstitial lung disease (ILD)).

Other methods embodied herein include generating a microRNA profile from a biological sample. The microRNA profile comprises the amounts of specific microRNA sequences. The amounts of those specific microRNA sequences are then compared to a reference to provide information for detecting or predicting the lung condition. In this regard, the microRNA profile may include those specific microRNA sequences identified below in the examples, or a subset thereof. Such microRNA profiles would be smaller, faster, and provide the same diagnostic information as larger test kits.

The following examples are provided to illustrate the devices and methods of the present disclosure. The examples are merely illustrative and are not intended to limit the disclosure to the materials, conditions, or process parameters set forth therein.

EXAMPLES Isolation of microRNA

microRNA can be isolated using glass filter based methods to selectively bind RNA in a high salt buffer. The unwanted biomolecules can then be washed off by using high salt buffers containing at least 50% alcohol. The bound pure RNA can then eluted off the glass membrane with low salt buffer or RNAse-free water.

1). Isolating microRNA from Solid Tissues

Briefly, total RNA, including microRNA, was isolated using commercial kits such as miRNeasy mini kit (Qiagen Inc. Valencia, Calif.). Approximately 5 mg to 50 mg tissue samples were excised from flash-frozen tissue. After placing the tissue sample into a Dounce tissue grinder, 700 microliter (μl) QIAzol lysis reagent was added to the grinder and the tissue was homogenized immediately. For every 700 μl QIAzol lysis reagent used, 140 μl chloroform was added to the tissue lysate to extract the water soluble content. After mixing for 15 seconds, the lysate was placed in a centrifuge and spun at 12000×g for 15 minutes at room temperature. The upper aqueous phase (containing the RNA) was then transferred to a new collection tube, and 1.5 volumes of ethanol was added. The sample was then transferred to a cartridge containing a glass filter (i.e. silica membrane) so that RNA could attach to the glass filter. The contaminants were washed off the silica membrane by applying different high salt washing buffers included in the miRNeasy kit. The bound pure RNA was then eluted off the membrane with water or low salt buffer.

2). Isolating microRNA from Liquid Samples

Approximately, 800 μl of QIAzol lysis reagent was added to 200 μl liquid sample. The sample was mixed in a tube followed by adding 200 μl of chloroform. After mixing rigorously for 15 seconds, the sample was then centrifuged at 12,000×g for 15 minutes. The upper aqueous phase was carefully transferred to a new collection tube, and 1.5 total volumes of ethanol was added. The sample was then applied directly to a glass membrane containing column and the RNA was bound and purified by three contiguous washing to remove unwanted contamination. The immobilized RNA was then collected from the membrane with a low salt elution buffer.

The yield of microRNA from different amount of liquid samples used in these protocols was tested. The best ratio was found to be 4 volumes of lysis buffer with 1 volume of liquid sample.

The quality and quantity of RNA isolated was evaluated by RNA by NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Mass.) and the Agilent 2100 Bioanalyzer (Agilent Inc. Santa Clara, Calif.).

FIG. 1A shows an electropherogram of RNA isolated from solid tissue, while FIG. 1B shows an electropherogram of RNA isolated from a liquid sample. The 18S and 28S peaks are clearly visible and marked. The microRNA are located on the left of both electropherograms. This region also contains all degraded RNA.

Array Hybridization and Quantitative PCR

Agilent's human and mouse microRNA microarray kits (Agilent Inc. Santa Clara, Calif.) were used as the array platform; however, arrays from different companies including Affymetrix and Exiqon have also been used. The human microRNA microarray contained probes for 723 human and 76 human viral microRNAs from the Sanger database v 10.1. The mouse microRNA microarray contained probes for 567 mouse and 10 mouse herpes virus microRNA sequences from the Sanger database v 10.1. Cyanine 3-pCp labeled RNA (i.e. RNA labeled with Cyanine 3-Cytidine bisphosphate) for array hybridization was generated by 100 nanograms (ng) of total RNA using Agilent's microRNA complete labeling and hybridization kit. All the steps, including labeling, hybridization, washing, scanning and feature extraction were performed in accordance with the manufacturer's instructions.

In brief, 100 ng of total RNA was dephosphorylated with calf intestinal alkaline phosphatase, then heat and DMSO treated to yield denatured RNA. Cyanine 3-Cytidine bisphosphate was joined to the microRNA by T4 RNA ligase. MicroBioSpin 6 columns were used to desalt the samples and remove any unincorporated fluorophores. The samples were hybridized to 8×15K Agilent Human microRNA (V2) or Mouse microRNA microarrays in a rotating hybridization oven for 20 hours at 55° C. and 10 rpm. The arrays were washed for 5 minutes in Agilent GE Wash Buffer 1 with Triton X-102 and then for another 5 minutes in Agilent GE Wash Buffer 2 with Triton X-102.

After washing, all slides were immediately scanned using a PerkinElmer ScanArray Express at 5 micron resolution. The resulting images were quantified using Agilent's Feature Extraction software. The differentially expressed microRNA sequences were then identified using a standard protocol developed for gene array data processing. The sample or gene clustering and array hybridization heatmap were generated using MeV4 software package from The Institute for Genomic Research (TIGR) (available at http://www.tigr.org/tdb/microarray/).

Quantitative PCR (QPCR) with microRNA specific primer sets were used to confirm the results from array hybridization. In brief, a SYBR Green based method, miScript real-time PCR (Qiagen Inc. Valencia, Calif., USA), or TaqMan primer set from Apply Biosystems, was used with 50 ng of total RNA from each sample. The first strand cDNA was generated according to the manufacturer's instruction. Approximately 2.5 ng of cDNA was used in the PCR reaction. The yield of 18 to 20 base pair fragments (based on SYBR Green intensity) corresponding to the specific microRNA species was monitored with the 7900HT fast real-time PCR system from Applied Biosystems (Applied Biosystems, Foster City, Calif.). QPCR results were analyzed by SDS 2.2.2, with a manual CT threshold value of 0.2.

Example 1

This example showed that microRNA sequences could be used as a marker to detect liver injury. Mice were used as the experimental model.

6-month-old male C57/B6 mice were grouped into control and treatment groups with 4 animals in each group. The mice then fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in phosphate buffer saline (PBS) (treatment group); (b) or PBS (control group). Mice were sacrificed at different time points post-exposure (12 hr, 24 hr, 48 hr, 72 hr, and 120 hr) and plasma and liver samples were collected. Part of the liver samples were sectioned and examined by a pathologist and the serum alanine transaminase (ALT) levels were also determined to confirm as well as assess the severity of liver injury.

Total RNA was isolated from collected samples to conduct comprehensive microRNA analyses. To assess the level of microRNA in liver tissues, a microRNA array from Agilent was used. The RNA samples were labeled and processed according to the manufacturer's recommended protocols. The data from each array were extracted, normalized and compared following a standard gene expression microarray method.

The expression levels of various microRNA sequences in the liver tissues were used to generate a microRNA profile and used to assess tissue injury. Differentially expressed microRNA sequences were clustered using the Hierarchical clustering method and the result is shown in FIG. 2. The different time points are indicated on the top, while the identity of individual microRNA sequences is listed on the right. (The identifying labels correspond to those in the miRNA Registry maintained at the Sanger Institute.) The hybridization intensity of individual microRNA sequences is represented in different colors as indicated on top of the figure (yellow representing the highest expression and blue representing the lowest expression signal). The microRNA profile clearly indicates that the levels of some microRNA sequences were changed by the exposure to acetaminophen.

Example 2

This example showed that the levels of specific microRNA sequences in the serum or plasma could be used to assess drug-induced liver injury.

The male C57/B6 mice were randomly grouped into two groups, a treatment group (3 animals) and control group (4 animals). They fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in PBS (treatment group); or (b) PBS (control group). Mice were sacrificed at 24 hours post exposure, the plasma samples were collected and RNA was isolated.

The expression levels of microRNA sequences in the serum were used to make a microRNA profile. The differentially expressed microRNA sequences between the treatment group and the control group were clustered with the Hierarchical clustering method and is shown in FIG. 3. The result clearly indicated that the levels of certain microRNA sequences in the serum could be used as an indication of the acetaminophen toxicity.

Example 3

This example showed that the levels of specific microRNA sequences in the serum or plasma could be used as an early indication of drug-induced liver injury.

The male C57/B6 mice were randomly grouped into nine different groups with 4 animals in each group. They fasted for 24 hours prior to a single intraperitoneal injection with either (a) 75 mg/Kg of acetaminophen in PBS (treatment 1); (b) 150 mg/Kg of acetaminophen in PBS (treatment 2); (c) 300 mg/Kg of acetaminophen in PBS (treatment 3); or (d) PBS (control group). Mice were sacrificed and plasma samples were collected at 1, 3 and 24 hours post-exposure. The nine groups were: 1) 1 hour control; 2) 1 hour treatment 1; 3) 1 hour treatment 2; 4) 1 hour treatment 3; 5) 3 hour control; 6) 3 hour treatment 1; 7) 3 hour treatment 2; 8) 3 hour treatment 3; and 9) 24 hour treatment 3. The group at 24 hr post-exposure received only the highest dose (300 mg/kg) to serve as a positive control. The expression levels of two different microRNA sequences, mir-486 and mir-122, in the serum were profiled by quantitative polymerase chain reactions (Q-PCR).

The median intensities (Z-axes) from each group (X-axis) at PCR cycle number 19 were plotted. This graph is shown in FIG. 4. Both mir-486 (red bars) and mir-122 (green bars) intensities showed dose-dependent changes at 3 hr post-exposure. The intensity of mir-122 at 300 mg/kg was almost the same between 3 hr and 24 hr post-exposure. Clear changes were observed in the samples obtained at one hour post-acetaminophen injection. The results clearly indicated that the levels of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.

Next, the ratios of the median intensities (green bars) and average intensities (blue bars) from each group at PCR cycle number 19 were plotted. This graph is shown as FIG. 5. As expected, the ratios of both median and average intensities showed dose-dependent changes at 3 hr post-exposure. The ratio also clearly indicated the difference between 3 hr and 24 hr post-exposure. This result clearly indicated the ratio of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.

Example 4

This example showed that microRNA could be used in assessing neurological disorders. The microRNA expression patterns in brain tissues obtained from normal and prion infected animals were profiled as described above. The results are shown in FIG. 6. The result clearly indicated differences between normal and diseased samples.

Example 5

This example showed that microRNA could be used in assessing the health status of lungs. The microRNA expression patterns in lung tissues obtained from normal and diseased animals were profiled as described above. The results are shown in FIG. 7. The result clearly indicated there were differences on microRNA expression as the disease progressed (from 1 to 6 where 6 has the most serious disease condition) and a number of microRNA sequences are different between normal and disease samples. Thus, specific microRNA sequences or a panel of microRNA sequences could be used as a tool to assess the health status of lungs.

Example 6

This example showed that different biological pathways or compartments had very different microRNA profiles. The microRNA profiles in serum and urine samples obtained from a normal mouse were profiled as described above, then compared. The result is shown in FIG. 8. The result clearly revealed a significant difference in the microRNA composition in different body fluids. This would allow the development of different biomarkers to be used in different body fluids to assess the health status of tissues. In addition, microRNA sequences in a specific body fluid can be used as a reliable tool to assess the health status of tissues intimately associated with that body fluid, e.g. bladder and kidney tissues to the urine.

Example 7

miRNA profiles from lung tissue and plasma from ILD patients and COPD patients were compared to a control set of miRNA profiles from uninvolved lung tissue obtained from lung cancer resections (controls) and a control set of miRNA profiles from plasma samples obtained from clinically normal donors (collected by the Marsh lab). The miRNA profiles were compared in various pairwise combinations to determine which miRNA sequences were overexpressed and thus useful for diagnostic purposes. The miRNA profiles were obtained using a microarray kit available from Agilent, which generally detected a given miRNA with usually two independent oligonucleotide (oligo) targets and four or more in some cases.

FIG. 9 shows the graph comparing miRNA expression levels in control plasma with ILD plasma. Note the log scale. MiRNA with expression values that differ substantially between the two samples reside away from the diagonal line (i.e. y=x) that would represent equivalent expression in the two samples. This graph indicated that many miRNAs in ILD plasma are expressed at substantially higher levels than in the control plasma. There also appear to be a few miRNAs in the control profile that were expressed at relatively lower levels than in the ILD profile.

Next, in order to reduce the complexity of the data, the similarity of the signals returned by the different oligos that were present on the Agilent array and designed to detect a given miRNA were examined. For example, miRNA 1225-5p (i.e. mir-1225-5p) was about 3-fold over expressed in ILD plasma. For mir-1225-5p, four probe oligos were used in the Agilent array. FIG. 10A shows the signal in the ILD plasma samples (n=24). FIG. 10B shows the signal in the control plasma samples (n=6). As seen, all four probe oligos gave signals in the ILD and control plasma samples. In addition, the signal strengths were within a factor of about two to each other, even though these oligos differ slightly in sequence.

17 miRNA sequences were identified that appeared to be overexpressed in the ILD plasma samples. FIG. 11 shows the signal strength for all of the oligos that targeted these 17 sequences. The ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. As seen, the signal strengths for all of the independent oligo probes were reasonably close (i.e. within a factor of 2.5).

Since the signals from the independent probes were close, the data from all probes was combined. Then, the mean expression level for each miRNA was calculated and miRNA sequences which were relatively overexpressed in the ILD plasma samples were identified. (An alternative analysis path could have been to choose the data from one or two of the independent probes and identify overexpressed miRNA sequences based on that data.)

FIG. 12 shows the resulting graph with the mean and one standard deviation identified. Again, the ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. The expression of the displayed microRNAs was at least two-fold higher in the ILD plasma samples than in the control plasma samples (2× was an arbitrary value). Table 3 lists the specific data of FIG. 12.

TABLE 3 mean mean mean ILD/ microRNA level St. Dev. level St. Dev. mean sequence (ILD) (ILD) (control) (control) control hsv1-miR-H1 371 100 207 13.9 1.8 hsa-miR-223 688 317 338 105 2.0 hsa-miR-575 416 17 198 21.4 2.1 hsa-miR-483-5p 589 179 259 70.1 2.3 hsa-miR-150* 659 142 267 35.6 2.5 hsa-miR-22 958 589 376 91 2.5 hsa-miR-1225-5p 3361 873 1201 324.4 2.8 hsa-miR-939 644 463 224 36.5 2.9 hsa-miR-135a* 499 127 172 33.2 2.9 hsa-miR-940 1316 906 341 45.6 3.9 hsa-miR-134 830 9 201 50 4.1 hcmv-miR-UL70-3p 721 129 166 25.2 4.3 hsa-miR-630 3349 65 683 100.3 4.9 hsv1-miR-LAT 1250 490 223 64.2 5.6 kshv-miR-K12-3 3542 2912 588 422.3 6.0 hsa-miR-638 18055 11123 1670 716.2 10.8 hsa-miR-923 42215 40796 310 9883.1 136.2

Based on the standard deviations, the 17 miRNAs that met this criterion can be divided into three groups. 11 miRNAs (UL70-3p, 1225-5p, 134, 135a*, 150*, 483-5p, 575, 630, 638, H1, and LAT) were likely to be differentially expressed between ILD and control with high confidence. There was intermediate confidence for 4 miRNAs (mir-22, 223, 939, and 940); and lower confidence for miRNAs 923 and K12-3.

The degree of over expression displayed by these miRNAs varied over 100-fold, as shown in FIG. 13. Note the log scale.

Next, the miRNA that were expressed at a higher level in control plasma than ILD plasma were investigated. An arbitrary expression level of 250 or greater and 3.0 fold or greater relative overexpression as used to screen out marginal miRNA candidates. Three miRNA sequences passed this screen as shown in Table 4.

TABLE 4 mean mean mean control/ microRNA level St. Dev. level St. Dev. mean sequence (ILD) (ILD) (control) (control) ILD hsa-miR-451 729 1695.917 5274 5362.923 7 oligo 1 hsa-miR-451; 487 1096.762 3527.167 3421.076 7 oligo 2 hsa-miR-451; 390 375 5274 13.5 oligo 1 w/o outlier hsa-miR-451; 268 243 3527 13.1 oligo 2 w/o outlier hsa-miR-448 64 38.3 253 93 4 hsa-miR-92a-2* 78 46.94568 253.6667 109.7154 3

hsa-miR-448 and hiss-miR-92-a-2* were just over the threshold for inclusion and showed low absolute expression. The standard deviation for hsa-miR-451 was rather large when all samples (n=24) were used. However, when one outlier was removed (n=23), the standard deviations improved, as did the ratios. hsa-miR-451 was expressed ten times higher in control plasma relative to ILD plasma.

One of the miRNAs that is overexpressed in control serum relative to ILD serum, in combination with a miRNA that is overexpressed in ILD serum relative to control serum, could be used in a simple “top scoring pair” test for ILD.

Example 8

Using the same data as in Example 7, the expression of miRNA in both ILD plasma and ILD tissue was examined. That graph is shown in FIG. 14. While many miRNAs that were expressed in ILD plasma had little or no expression in ILD tissue, most of those that were expressed in tissue had at least some expression in plasma. Those miRNA sequences that had signal strength of at least 1000 in both tissue and plasma (an arbitrarily chosen value) are listed in Table 5. The ratio of the expression for the miRNA sequence was also compared to the average expression of all the miRNAs in the sample and is labeled as “overexpression ratio.”

TABLE 5 mean mean microRNA level overexpression level overexpression sequence (plasma) ratio (tissue) ratio hsa-miR-1225-5p 4043 9.8 1189.0 3.1 hsa-miR-21 455 1.1 9938.7 26.0 hsa-miR-22 1374 3.3 5555.8 14.5 hsa-miR-223 835 2.0 1303.4 3.4 hsa-miR-451 729 1.8 6564.4 17.2 hsa-miR-638 25920 62.6 1084.6 2.8 hsa-miR-923 71062 171.7 6114.7 16.0 kshv-miR-K12-3 5602 13.5 498.0 1.3

hsa-miR-21 was present here, but not in Tables 3 or 4, while the other seven were also listed in either Table 3 or 4.

Example 9

Using the same data as in Example 7, the expression of miRNA in both ILD lung tissue and control lung tissue was examined. That graph is shown in FIG. 15. The expression levels for certain miRNA sequences, as well as those overexpressed in ILD lung tissue, are listed below in Table 6.

TABLE 6 microRNA mean level mean level mean ILD/ sequence (ILD) (control) mean control hsa-miR-923 6114.7 22421.33 0.3 hsa-miR-22 5555.8 4771.83 1.2 hsa-miR-29a 4881.6 2183.67 2.2 hsa-miR-145 3759.9 1449.50 2.6 hsa-miR-26a 3187.3 1123.33 2.8 hsa-let-7c 4405.5 1336.33 3.3 hsa-miR-23a 4396.1 1144.83 3.8 hsa-miR-21 9938.7 2450.83 4.1 hsa-miR-125b 3843.2 939.17 4.1 hsa-miR-27a 3090.2 720.50 4.3 hsa-let-7a 5709.2 1039.33 5.5 hsa-let-7f 3352.5 504.83 6.6 hsa-miR-451 6564.4 383.33 17.1

miRNA hsa-miR-923 was over 130-fold over-expressed in ILD plasma relative to control plasma (Table 3), but it is under-expressed in ILD lung tissue relative to control lung tissue (Table 6). This suggests the tissue or cell of origin for this miRNA may be within the blood itself, or at least not the ILD lung. Similarly, miRNA hsa-miR-22 is expressed three times higher in ILD plasma compared to control plasma (Table 3), but is expressed at nearly the same level in ILD lung and control lung tissue (Table 6). Other miRNAs that are characteristic of ILD, such as miRNA-451, were elevated in ILD lung tissue (17× in Table 6) but not in ILD plasma (see Table 4).

In total 17 miRNA sequences were identified as containing diagnostic information related to ILD. Those 17 sequences are listed in Table 3.

Example 10

The plasma sample data (control, COPD, and ILD) was separately analyzed using the Panorama suite of tools and consisted of the following steps: (A) Normalization; (B) Quality Control; (C) Cluster Analysis; (D) Panel Selection; and (E) Comparison. Each step is explained in more detail below.

In Normalization, the following steps occurred. First, missing values were left unchanged instead of imputing a value. Second, each sample was normalized independently of other samples. Third, the natural log was applied to the values for each sample; then the values were adjusted by the median and standard deviation. FIG. 16 shows the results of normalization.

In Quality Control, the quality of the data was assessed before and after normalization. FIG. 17A shows the Pearson correlation distribution before normalization. This figure correlated the score for each miRNA across the samples to the total miRNA expression level across the samples. This figure showed that the vast majority of miRNA sequences had the same expression profile across the samples, and furthermore, this expression profile is the total miRNA level per sample—this is the dominant feature of the dataset. FIG. 17B shows the Pearson correlation distribution after normalization. Normalization improved the quality of the dataset. The distribution in FIG. 17B was much less skewed than that of FIG. 17A.

In Cluster Analysis, the normalized miRNA data was clustered using multi-dimensional scaling (MDS); the results are presented in FIG. 18. This was an unsupervised analysis without samples being identified by group or miRNA selected that differentiated the groups. There were several notable features of this plot. First, the samples within each group clustered together showing uniformity in miRNA expression. The exception was IPF tissue where a few outliers occurred, likely due to sample handling. Second, the tissue groups cluster away from the plasma groups. Third, within the plasma groups, the COPD and ILD plasma samples overlapped and were clustered away from the plasma control samples.

Performing a T-test at significance level 0.01, 194 miRNA were found to separate the plasma control samples from the ILD plasma samples. Performing 50 permutation tests revealed that the expected number of miRNA, by chance alone, was 21.5, yielding a false discovery rate of 11%. The p-value distribution of all miRNA is shown in FIG. 19. There was a uniform distribution over most p-values, except an increase below 0.05. This was consistent with the hypothesis that there are miRNA that segregate the two sample groups.

In Panel Selection, panels that segregated the control plasma samples from the ILD plasma samples were selected using Area Under the Curve (AOC). AOC is a measure of diagnostic segregation. It ranges from 0 to 1 where 1 indicates perfect segregation. The AUC of individual miRNA can be determined independently of each other allowing for straightforward selection of the best segregating miRNA. In addition, the combined AUC of panels of miRNA can be calculated to assess how well groups of miRNA work together to segregate control plasma samples from ILD plasma samples.

To calculate the combined AUC, a combination rule must be established. The combination rule used here was majority consensus: if the strict majority of miRNA classified a sample as diseased (i.e. ILD or IPF) then the sample was classified diseased, otherwise, the sample was classified as normal.

FIG. 20 is three charts showing the distribution of directional bias (upper left), the AUC distribution (upper right), and the standard deviation for the ILD group (lower left). The number of miRNA higher in the control samples than the ILD samples was essentially the same as the opposite direction. The distribution of AUC scores for all miRNA was centered about 0.6 which is expected. A small rise around 0.95 indicated the presence of miRNA that distinguish the control and ILD samples. The distribution of miRNA expression standard deviations showed that overall, variability was similar across miRNA (note that normalization is done by sample, not by miRNA).

In Comparison, the data was analyzed. Using an AUC threshold of 0.95, 57 out of 2421 (2.4%) miRNA probes were selected. Table 7 contains the oligo probe used for the miRNA, the corresponding miRNA, p-value, AUC, and number of panels of 3 miRNA above combined AUC 0.99 that each miRNA participated in. If the miRNA was expressed higher in the control sample than the ILD sample, the column “Control>ILD” was marked with a Y.

TABLE 7 Probe miRNA Control > ILD P-value AUC St. Dev. # Panels A_25_P00010804 hsa-miR-518d-3p Y 9.96E−06 1.00 0.42 945 A_25_P00013406 hsa-miR-135a* N 1.56E−09 1.00 0.55 681 A_25_P00013825 hiv1-miR-H1 N 4.98E−06 1.00 0.66 656 A_25_P00011724 hcmv-miR-UL70-3p N 1.23E−13 1.00 0.62 621 A_25_P00013407 hsa-miR-135a* N 1.41E−12 1.00 0.62 615 A_25_P00013090 hsa-miR-940 N 8.13E−09 1.00 0.88 581 A_25_P00012074 hsa-miR-139-3p N 3.27E−07 1.00 0.63 572 A_25_P00013689 kshv-miR-K12-3 N 1.09E−11 1.00 0.60 572 A_25_P00012231 hsa-miR-134 N 9.11E−11 1.00 0.64 548 A_25_P00012230 hsa-miR-134 N 6.22E−13 1.00 0.65 539 A_25_P00010345 hsa-miR-557 N 2.47E−06 1.00 0.55 534 A_25_P00013829 hsv1-miR-LAT N 1.52E−11 1.00 0.77 500 A_25_P00011725 hcmv-miR-UL70-3p N 6.62E−13 1.00 0.65 463 A_25_P00013830 hsv1-miR-LAT N 1.42E−12 1.00 0.65 449 A_25_P00013831 hsv1-miR-LAT N 2.62E−09 1.00 0.84 449 A_25_P00013087 hsa-miR-939 N 3.19E−13 1.00 0.53 362 A_25_P00013453 hsa-miR-150* N 1.67E−08 1.00 0.58 362 A_25_P00014907 hsa-miR-1224-5p N 6.43E−07 1.00 0.47 344 A_25_P00013326 hsa-miR-187* N 1.24E−06 1.00 0.70 324 A_25_P00013828 hsv1-miR-LAT N 1.33E−12 1.00 0.80 299 A_25_P00011853 ebv-miR-BART13 N 4.09E−08 0.99 0.47 435 A_25_P00015004 hsa-miR-1226* N 2.14E−06 0.99 0.66 362 A_25_P00010687 hsa-miR-498 N 7.82E−08 0.99 0.46 498 A_25_P00011096 hsa-miR-572 N 3.25E−08 0.99 0.81 420 A_25_P00010808 hsa-miR-575 N 2.42E−07 0.99 0.85 415 A_25_P00014908 hsa-miR-1224-5p N 4.81E−07 0.99 0.60 344 A_25_P00014896 hsa-miR-575 N 4.23E−07 0.99 0.82 316 A_25_P00010641 hsa-miR-601 N 2.63E−08 0.99 0.49 218 A_25_P00013086 hsa-miR-939 N 1.09E−07 0.99 0.49 179 A_25_P00013450 hsa-miR-150* N 2.91E−08 0.99 0.67 178 A_25_P00010344 hsa-miR-557 N 5.45E−06 0.98 0.65 684 A_25_P00013327 hsa-miR-187* N 1.06E−05 0.98 0.65 347 A_25_P00015003 hsa-miR-1226* N 2.15E−06 0.98 0.61 179 A_25_P00013451 hsa-miR-150* N 1.39E−06 0.98 0.67 178 A_25_P00014906 hsa-miR-1224-5p N 2.03E−07 0.97 0.55 330 A_25_P00012059 hsa-miR-198 N 1.13E−05 0.97 0.67 296 A_25_P00011799 hsv1-miR-H1 N 6.61E−07 0.97 0.98 268 A_25_P00011097 hsa-miR-572 N   6E−05 0.97 0.60 203 A_25_P00013452 hsa-miR-150* N 8.41E−07 0.97 0.62 179 A_25_P00010669 hsa-miR-326 N 7.84E−05 0.97 0.83 177 A_25_P00014892 hsa-miR-539 N 0.000543 0.97 0.59 722 A_25_P00010444 hsa-miR-448 N 6.72E−05 0.97 0.58 581 A_25_P00012030 hsa-miR-92a N 1.18E−05 0.97 0.86 343 A_25_P00013448 hsa-miR-149* N 1.95E−05 0.97 0.58 260 A_25_P00014861 hsa-miR-483-5p N  2.9E−07 0.97 0.62 144 A_25_P00010228 hsa-miR-623 N  5.7E−05 0.96 0.79 356 A_25_P00012419 hsa-miR-423-5p N 0.000569 0.96 0.87 336 A_25_P00011796 hsv1-miR-H1 N 2.96E−06 0.96 0.69 268 A_25_P00011854 ebv-miR-BART13 N 0.000141 0.96 0.73 268 A_25_P00011719 ebv-miR-BART7 N 7.87E−05 0.96 0.57 224 A_25_P00012459 hsa-miR-483-5p N 2.03E−07 0.96 0.66 178 A_25_P00013449 hsa-miR-149* N 1.55E−06 0.96 0.66 164 A_25_P00012262 hsa-miR-320 N 5.41E−05 0.96 0.73  84 A_25_P00011342 hsa-miR-765 N 5.08E−06 0.96 0.38  57 A_25_P00013324 hsa-miR-187* N 5.29E−05 0.95 0.68 477 A_25_P00010227 hsa-miR-623 N 4.89E−05 0.95 0.97 362 A_25_P00012031 hsa-miR-92a N 2.59E−05 0.95 1.07 343

Interestingly, only 3 of the 57 miRNA were higher in the control samples than the ILD samples, despite the near equivalence of miRNA higher in control samples over ILD samples, as compared to the opposite among all miRNA (see FIG. 20). 20 of the 57 miRNA had a perfect AUC score of 1.00. Not shown here is the fact that there were also many panels of three miRNA that had a perfect AUC score of 1.00.

There were also unique miRNA among the 57 miRNA probes, which illustrated a strong redundancy among probes. This redundancy could be used as a selection criterion.

11 of the 17 miRNA sequences listed in Table 3 of Example 7 also appear in Table 7. They are shown in bold text in Table 7.

The claims refer to identifying “at least one” or “at least two” differentially expressed microRNA sequences in a microRNA profile, wherein the differentially expressed microRNA sequences are selected from a list. This language should be construed as meaning that the microRNA sequence selected from the list is identified as a differentially expressed microRNA sequence in the microRNA profile.

It is contemplated that assays or microRNA profiles would test for only specific microRNA sequences, such as those identified above.

In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-22, miR-223, miR-483-5p, miR-575, miR-638, miR-923, miR-939, miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 17 of these sequences are tested for. Specific pairs of these 17 microRNA sequences include those listed in Table 8:

TABLE 8 miR-630, miR-134 miR-630, hcmv-miR-UL70-3p miR-630, miR-1225-5p miR-630, miR-135a* miR-630, miR-150* miR-630, miR-22 miR-630, miR-223 miR-630, miR-483-5p miR-630, miR-575 miR-630, miR-638 miR-630, miR-923 miR-630, miR-939 miR-630, miR-940 miR-630, hsv1-miR-H1 miR-630, hsv1-miR-LAT miR-630, kshv-miR-K12-3 miR-134, hcmv-miR-UL70-3p miR-134, miR-1225-5p miR-134, miR-135a* miR-134, miR-150* miR-134, miR-22 miR-134, miR-223 miR-134, miR-483-5p miR-134, miR-575 miR-134, miR-638 miR-134, miR-923 miR-134, miR-939 miR-134, miR-940 miR-134, hsv1-miR-H1 miR-134, hsv1-miR-LAT miR-134, kshv-miR-K12-3 hcmv-miR-UL70-3p, miR-1225-5p hcmv-miR-UL70-3p, miR-135a* hcmv-miR-UL70-3p, miR-150* hcmv-miR-UL70-3p, miR-22 hcmv-miR-UL70-3p, miR-223 hcmv-miR-UL70-3p, miR-483-5p hcmv-miR-UL70-3p, miR-575 hcmv-miR-UL70-3p, miR-638 hcmv-miR-UL70-3p, miR-923 hcmv-miR-UL70-3p, miR-939 hcmv-miR-UL70-3p, miR-940 hcmv-miR-UL70-3p, hsv1-miR-H1 hcmv-miR-UL70-3p, hsv1-miR-LAT hcmv-miR-UL70-3p, kshv-miR- miR-1225-5p, miR-135a* K12-3 miR-1225-5p, miR-150* miR-1225-5p, miR-22 miR-1225-5p, miR-223 miR-1225-5p, miR-483-5p miR-1225-5p, miR-575 miR-1225-5p, miR-638 miR-1225-5p, miR-923 miR-1225-5p, miR-939 miR-1225-5p, miR-940 miR-1225-5p, hsv1-miR-H1 miR-1225-5p, hsv1-miR-LAT miR-1225-5p, kshv-miR-K12-3 miR-135a*, miR-150* miR-135a*, miR-22 miR-135a*, miR-223 miR-135a*, miR-483-5p miR-135a*, miR-575 miR-135a*, miR-638 miR-135a*, miR-923 miR-135a*, miR-939 miR-135a*, miR-940 miR-135a*, hsv1-miR-H1 miR-135a*, hsv1-miR-LAT miR-135a*, kshv-miR-K12-3 miR-150*, miR-22 miR-150*, miR-223 miR-150*, miR-483-5p miR-150*, miR-575 miR-150*, miR-638 miR-150*, miR-923 miR-150*, miR-939 miR-150*, miR-940 miR-150*, hsv1-miR-H1 miR-150*, hsv1-miR-LAT miR-150*, kshv-miR-K12-3 miR-22, miR-223 miR-22, miR-483-5p miR-22, miR-575 miR-22, miR-638 miR-22, miR-923 miR-22, miR-939 miR-22, miR-940 miR-22, hsv1-miR-H1 miR-22, hsv1-miR-LAT miR-22, kshv-miR-K12-3 miR-223, miR-483-5p miR-223, miR-575 miR-223, miR-638 miR-223, miR-923 miR-223, miR-939 miR-223, miR-940 miR-223, hsv1-miR-H1 miR-223, hsv1-miR-LAT miR-223, kshv-miR-K12-3 miR-483-5p, miR-575 miR-483-5p, miR-638 miR-483-5p, miR-923 miR-483-5p, miR-939 miR-483-5p, miR-940 miR-483-5p, hsv1-miR-H1 miR-483-5p, hsv1-miR-LAT miR-483-5p, kshv-miR-K12-3 miR-575, miR-638 miR-575, miR-923 miR-575, miR-939 miR-575, miR-940 miR-575, hsv1-miR-H1 miR-575, hsv1-miR-LAT miR-575, kshv-miR-K12-3 miR-638, miR-923 miR-638, miR-939 miR-638, miR-940 miR-638, hsv1-miR-H1 miR-638, hsv1-miR-LAT miR-638, kshv-miR-K12-3 miR-923, miR-939 miR-923, miR-940 miR-923, hsv1-miR-H1 miR-923, hsv1-miR-LAT miR-923, kshv-miR-K12-3 miR-939, miR-940 miR-939, hsv1-miR-H1 miR-939, hsv1-miR-LAT miR-939, kshv-miR-K12-3 miR-940, hsv1-miR-H1 miR-940, hsv1-miR-LAT miR-940, kshv-miR-K12-3 hsv1-miR-H1, hsv1-miR-LAT hsv1-miR-H1, kshv-miR-K12-3 hsv1-miR-LAT, kshv-miR-K12-3

In other embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 9:

TABLE 9 miR-630, miR-134 miR-630, hcmv-miR-UL70-3p miR-630, miR-1225-5p miR-630, miR-135a* miR-630, miR-150* miR-630, miR-483-5p miR-630, miR-575 miR-630, miR-638 miR-630, hsv1-miR-H1 miR-630, hsv1-miR-LAT miR-134, hcmv-miR-UL70-3p miR-134, miR-1225-5p miR-134, miR-135a* miR-134, miR-150* miR-134, miR-483-5p miR-134, miR-575 miR-134, miR-638 miR-134, hsv1-miR-H1 miR-134, hsv1-miR-LAT hcmv-miR-UL70-3p, miR-1225-5p hcmv-miR-UL70-3p, miR-135a* hcmv-miR-UL70-3p, miR-150* hcmv-miR-UL70-3p, miR-483-5p hcmv-miR-UL70-3p, miR-575 hcmv-miR-UL70-3p, miR-638 hcmv-miR-UL70-3p, hsv1-miR-H1 hcmv-miR-UL70-3p, hsv1-miR-LAT miR-1225-5p, miR-135a* miR-1225-5p, miR-150* miR-1225-5p, miR-483-5p miR-1225-5p, miR-575 miR-1225-5p, miR-638 miR-1225-5p, hsv1-miR-H1 miR-1225-5p, hsv1-miR-LAT miR-135a*, miR-150* miR-135a*, miR-483-5p miR-135a*, miR-575 miR-135a*, miR-638 miR-135a*, hsv1-miR-H1 miR-135a*, hsv1-miR-LAT miR-150*, miR-483-5p miR-150*, miR-575 miR-150*, miR-638 miR-150*, hsv1-miR-H1 miR-150*, hsv1-miR-LAT miR-483-5p, miR-575 miR-483-5p, miR-638 miR-483-5p, hsv1-miR-H1 miR-483-5p, hsv1-miR-LAT miR-575, miR-638 miR-575, hsv1-miR-H1 miR-575, hsv1-miR-LAT miR-638, hsv1-miR-H1 miR-638, hsv1-miR-LAT hsv1-miR-H1, hsv1-miR-LAT

In some embodiments, an assay or microRNA profile tests for two or more microRNA sequences. At least one of the microRNA sequences tested for is selected from the group consisting of miR-630, hcmv-miR-UL70-3p, miR-1225-5p, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. At least one of the microRNA sequences tested for is selected from the group consisting of miR-451, miR-448, and miR-92a-2*. In particular embodiments, miR-451 is one of the microRNA sequences tested for. Specific pairs of these microRNA sequences include those listed in Table 10:

TABLE 10 miR-630, miR-451 miR-630, miR-448 miR-630, miR-92a-2* hcmv-miR-UL70-3p, miR-451 hcmv-miR-UL70-3p, miR-448 hcmv-miR-UL70-3p, miR-92a-2* miR-1225-5p, miR-451 miR-1225-5p, miR-448 miR-1225-5p, miR-92a-2* miR-134, miR-451 miR-134, miR-448 miR-134, miR-92a-2* miR-135a*, miR-451 miR-135a*, miR-448 miR-135a*, miR-92a-2* miR-150*, miR-451 miR-150*, miR-448 miR-150*, miR-92a-2* miR-483-5p, miR-451 miR-483-5p, miR-448 miR-483-5p, miR-92a-2* miR-575, miR-451 miR-575, miR-448 miR-575, miR-92a-2* miR-638, miR-451 miR-638, miR-448 miR-638, miR-92a-2* hsv1-miR-H1, miR-451 hsv1-miR-H1, miR-448 hsv1-miR-H1, miR-92a-2* hsv1-miR-LAT, miR-451 hsv1-miR-LAT, miR-448 hsv1-miR-LAT, miR-92a-2*

In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-451, miR-923, miR-1225-5p, miR-22, miR-223, miR-638, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all seven of these sequences are tested for. Specific pairs of these seven microRNA sequences include those listed in Table 11:

TABLE 11 miR-451, miR-923 miR-451, miR-1225-5p miR-451, miR-22 miR-451, miR-223 miR-451, miR-638 miR-451, kshv-miR-K12-3 miR-923, miR-1225-5p miR-923, miR-22 miR-923, miR-223 miR-923, miR-638 miR-923, kshv-miR-K12-3 miR-1225-5p, miR-22 miR-1225-5p, miR-223 miR-1225-5p, miR-638 miR-1225-5p, kshv-miR-K12-3 miR-22, miR-223 miR-22, miR-638 miR-22, kshv-miR-K12-3 miR-223, miR-638 miR-223, kshv-miR-K12-3 miR-638, kshv-miR-K12-3

In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-940, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 12:

TABLE 12 miR-940, miR-134 miR-940, miR-135a* miR-940, miR-150* miR-940, miR-483-5p miR-940, miR-575 miR-940, miR-939 miR-940, hsv1-miR-H1 miR-940, kshv-miR-K12-3 miR-940, hsv1-miR-LAT miR-940, hcmv-miR-UL70-3p miR-134, miR-135a* miR-134, miR-150* miR-134, miR-483-5p miR-134, miR-575 miR-134, miR-939 miR-134, hsv1-miR-H1 miR-134, kshv-miR-K12-3 miR-134, hsv1-miR-LAT miR-134, hcmv-miR-UL70-3p miR-135a*, miR-150* miR-135a*, miR-483-5p miR-135a*, miR-575 miR-135a*, miR-939 miR-135a*, hsv1-miR-H1 miR-135a*, kshv-miR-K12-3 miR-135a*, hsv1-miR-LAT miR-135a*, hcmv-miR-UL70-3p miR-150*, miR-483-5p miR-150*, miR-575 miR-150*, miR-939 miR-150*, hsv1-miR-H1 miR-150*, kshv-miR-K12-3 miR-150*, hsv1-miR-LAT miR-150*, hcmv-miR-UL70-3p miR-483-5p, miR-575 miR-483-5p, miR-939 miR-483-5p, hsv1-miR-H1 miR-483-5p, kshv-miR-K12-3 miR-483-5p, hsv1-miR-LAT miR-483-5p, hcmv-miR-UL70-3p miR-575, miR-939 miR-575, hsv1-miR-H1 miR-575, kshv-miR-K12-3 miR-575, hsv1-miR-LAT miR-575, hcmv-miR-UL70-3p miR-939, hsv1-miR-H1 miR-939, kshv-miR-K12-3 miR-939, hsv1-miR-LAT miR-939, hcmv-miR-UL70-3p hsv1-miR-H1, kshv-miR-K12-3 hsv1-miR-H1, hsv1-miR-LAT hsv1-miR-H1, hcmv-miR-UL70-3p kshv-miR-K12-3, hsv1-miR-LAT kshv-miR-K12-3, hcmv-miR-UL70-3p hsv1-miR-LAT, hcmv-miR-UL70-3p

Appendix A provides a listing of the RNA sequences for all of the microRNA discussed herein, including human orthologs thereof.

APPENDIX A Accession SEQ ID miRNA name Number RNA Sequence No: ebv-miR-BART10* MIMAT0004817 gccaccucuuugguucuguaca 1 ebv-miR-BART12 MIMAT0003423 uccugugguguuuggugugguu 2 ebv-miR-BART13 MIMAT0003424 uguaacuugccagggacggcuga 3 ebv-miR-BART13* MIMAT0004818 aaccggcucguggcucguacag 4 ebv-miR-BART15 MIMAT0003713 gucagugguuuuguuuccuuga 5 ebv-miR-BART1-5p MIMAT0000999 ucuuaguggaagugacgugcugug 6 ebv-miR-BART16 MIMAT0003714 uuagauagagugggugugugcucu 7 ebv-miR-BART18- MIMAT0003717 ucaaguucgcacuuccuauaca 8 5p ebv-miR-BART19- MIMAT0003718 uuuuguuugcuugggaaugcu 9 3p ebv-miR-BART19- MIMAT0004836 acauuccccgcaaacaugacaug 10 5p ebv-miR-BART20- MIMAT0003719 uagcaggcaugucuucauucc 11 5p ebv-miR-BART2-5p MIMAT0001000 uauuuucugcauucgcccuugc 12 ebv-miR-BART3* MIMAT0003410 accuaguguuaguguugugcu 13 ebv-miR-BART5 MIMAT0003413 caaggugaauauagcugcccaucg 14 ebv-miR-BART6-5p MIMAT0003414 uaagguugguccaauccauagg 15 ebv-miR-BART7 MIMAT0003416 caucauaguccaguguccaggg 16 ebv-miR-BART7* MIMAT0004815 ccuggaccuugacuaugaaaca 17 ebv-miR-BHRF1-1 MIMAT0000995 uaaccugaucagccccggaguu 18 ebv-miR-BHRF1-3 MIMAT0000998 uaacgggaaguguguaagcaca 19 hcmv-miR-UL148D MIMAT0001578 ucguccuccccuucuucaccg 20 hcmv-miR-UL22A MIMAT0001574 uaacuagccuucccgugaga 21 hcmv-miR-UL22A* MIMAT0001575 ucaccagaaugcuaguuuguag 22 hcmv-miR-UL70-3p MIMAT0003343 ggggaugggcuggcgcgcgg 23 hcmv-miR-UL70-5p MIMAT0003342 ugcgucucggccucguccaga 24 hcmv-miR-US25-1 MIMAT0001581 aaccgcucaguggcucggacc 25 hcmv-miR-US25-2- MIMAT0001583 auccacuuggagagcucccgcgg 26 3p hcmv-miR-US25-2- MIMAT0001582 agcggucuguucagguggauga 27 5p hcmv-miR-US4 MIMAT0003341 cgacauggacgugcagggggau 28 hiv1-miR-H1 MIMAT0004480 ccagggaggcgugccugggc 29 hiv1-miR-N367 MIMAT0004478 acugaccuuuggauggugcuucaa 30 hsa-miR-1 MIMAT0000416 ggaauguaaagaaguauguau 31 hsa-miR-10b MIMAT0000254 uacccuguagaaccgaauuugug 32 hsa-miR-122 MIMAT0000421 uggagugugacaaugguguuug 33 hsa-miR-1224-3p MIMAT0005459 ccccaccuccucucuccucag 34 hsa-miR-1224-5p MIMAT0005458 gugaggacucgggaggugg 35 hsa-miR-1225-3p MIMAT0005573 ugagccccugugccgcccccag 36 hsa-miR-1225-5p MIMAT0005572 guggguacggcccagugggggg 37 hsa-miR-1226* MIMAT0005576 gugagggcaugcaggccuggaugggg 38 hsa-miR-1227 MIMAT0005580 cgugccacccuuuuccccag 39 hsa-miR-1228 MIMAT0005583 ucacaccugccucgcccccc 40 hsa-miR-1229 MIMAT0005584 cucucaccacugcccucccacag 41 hsa-miR-1234 MIMAT0005589 ucggccugaccacccaccccac 42 hsa-miR-1237 MIMAT0005592 uccuucugcuccgucccccag 43 hsa-miR-1238 MIMAT0005593 cuuccucgucugucugcccc 44 hsa-miR-124 MIMAT0000422 uaaggcacgcggugaaugcc 45 hsa-miR-125a-3p MIMAT0004602 acaggugagguucuugggagcc 46 hsa-miR-125a-5p MIMAT0000443 ucccugagacccuuuaaccuguga 47 hsa-miR-127-3p MIMAT0000446 ucggauccgucugagcuuggcu 48 hsa-miR-127-5p MIMAT0004604 cugaagcucagagggcucugau 49 hsa-miR-128 MIMAT0000424 ucacagugaaccggucucuuu 50 hsa-miR-129* MIMAT0004548 aagcccuuaccccaaaaaguau 51 hsa-miR-129-3p MIMAT0004605 aagcccuuaccccaaaaagcau 52 hsa-miR-130a MIMAT0000425 cagugcaauguuaaaagggcau 53 hsa-miR-133a MIMAT0000427 uuugguccccuucaaccagcug 54 hsa-miR-133b MIMAT0000770 uuugguccccuucaaccagcua 55 hsa-miR-134 MIMAT0000447 ugugacugguugaccagagggg 56 hsa-miR-135a* MIMAT0004595 uauagggauuggagccguggcg 57 hsa-miR-136 MIMAT0000448 acuccauuuguuuugaugaugga 58 hsa-miR-136* MIMAT0004606 caucaucgucucaaaugagucu 59 hsa-miR-138 MIMAT0000430 agcugguguugugaaucaggccg 60 hsa-miR-139-3p MIMAT0004552 ggagacgcggcccuguuggagu 61 hsa-miR-140-3p MIMAT0004597 uaccacaggguagaaccacgg 62 hsa-miR-140-5p MIMAT0000431 cagugguuuuacccuaugguag 63 hsa-miR-141 MIMAT0000432 uaacacugucugguaaagaugg 64 hsa-miR-142-3p MIMAT0000434 uguaguguuuccuacuuuaugga 65 hsa-miR-143 MIMAT0000435 ugagaugaagcacuguagcuc 66 hsa-miR-146a MIMAT0000449 ugagaacugaauuccauggguu 67 hsa-miR-146b-3p MIMAT0004766 ugcccuguggacucaguucugg 68 hsa-miR-146b-5p MIMAT0002809 ugagaacugaauuccauaggcu 69 hsa-miR-148b MIMAT0000759 ucagugcaucacagaacuuugu 70 hsa-miR-150 MIMAT0000451 ucucccaacccuuguaccagug 71 hsa-miR-150* MIMAT0004610 cugguacaggccugggggacag 72 hsa-miR-15a* MIMAT0004488 caggccauauugugcugccuca 73 hsa-miR-15b MIMAT0000417 uagcagcacaucaugguuuaca 74 hsa-miR-181b MIMAT0000257 aacauucauugcugucggugggu 75 hsa-miR-181d MIMAT0002821 aacauucauuguugucggugggu 76 hsa-miR-183 MIMAT0000261 uauggcacugguagaauucacu 77 hsa-miR-185 MIMAT0000455 uggagagaaaggcaguuccuga 78 hsa-miR-186 MIMAT0000456 caaagaauucuccuuuugggcu 79 hsa-miR-187* MIMAT0004561 ggcuacaacacaggacccgggc 80 hsa-miR-188-5p MIMAT0000457 caucccuugcaugguggaggg 81 hsa-miR-190b MIMAT0004929 ugauauguuugauauuggguu 82 hsa-miR-191* MIMAT0001618 gcugcgcuuggauuucgucccc 83 hsa-miR-193b MIMAT0002819 aacuggcccucaaagucccgcu 84 hsa-miR-194 MIMAT0000460 uguaacagcaacuccaugugga 85 hsa-miR-198 MIMAT0000228 gguccagaggggagauagguuc 86 hsa-miR-199a-5p MIMAT0000231 cccaguguucagacuaccuguuc 87 hsa-miR-19a MIMAT0000073 ugugcaaaucuaugcaaaacuga 88 hsa-miR-200a MIMAT0000682 uaacacugucugguaacgaugu 89 hsa-miR-200b MIMAT0000318 uaauacugccugguaaugauga 90 hsa-miR-200b* MIMAT0004571 caucuuacugggcagcauugga 91 hsa-miR-200c MIMAT0000617 uaauacugccggguaaugaugga 92 hsa-miR-205 MIMAT0000266 uccuucauuccaccggagucug 93 hsa-miR-206 MIMAT0000462 uggaauguaaggaagugugugg 94 hsa-miR-208a MIMAT0000241 auaagacgagcaaaaagcuugu 95 hsa-miR-21 MIMAT0000076 uagcuuaucagacugauguuga 96 hsa-miR-211 MIMAT0000268 uucccuuugucauccuucgccu 97 hsa-miR-22 MIMAT0000077 aagcugccaguugaagaacugu 98 hsa-miR-220b MIMAT0004908 ccaccaccgugucugacacuu 99 hsa-miR-221 MIMAT0000278 agcuacauugucugcuggguuuc 100 hsa-miR-222 MIMAT0000279 agcuacaucuggcuacugggu 101 hsa-miR-223 MIMAT0000280 ugucaguuugucaaauacccca 102 hsa-miR-23b MIMAT0000418 aucacauugccagggauuacc 103 hsa-miR-26a MIMAT0000082 uucaaguaauccaggauaggcu 104 hsa-miR-27a MIMAT0000084 uucacaguggcuaaguuccgc 105 hsa-miR-27b MIMAT0000419 uucacaguggcuaaguucugc 106 hsa-miR-27b* MIMAT0004588 agagcuuagcugauuggugaac 107 hsa-miR-299-3p MIMAT0000687 uaugugggaugguaaaccgcuu 108 hsa-miR-299-5p MIMAT0002890 ugguuuaccgucccacauacau 109 hsa-miR-29b MIMAT0000100 uagcaccauuugaaaucaguguu 110 hsa-miR-29c* MIMAT0004673 ugaccgauuucuccugguguuc 111 hsa-miR-300 MIMAT0004903 uauacaagggcagacucucucu 112 hsa-miR-301b MIMAT0004958 cagugcaaugauauugucaaagc 113 hsa-miR-302c* MIMAT0000716 uuuaacauggggguaccugcug 114 hsa-miR-30a MIMAT0000087 uguaaacauccucgacuggaag 115 hsa-miR-30c MIMAT0000244 uguaaacauccuacacucucagc 116 hsa-miR-30c-1* MIMAT0004674 cugggagaggguuguuuacucc 117 hsa-miR-30e MIMAT0000692 uguaaacauccuugacuggaag 118 hsa-miR-31 MIMAT0000089 aggcaagaugcuggcauagcu 119 hsa-miR-323-3p MIMAT0000755 cacauuacacggucgaccucu 120 hsa-miR-324-3p MIMAT0000762 acugccccaggugcugcugg 121 hsa-miR-324-5p MIMAT0000761 cgcauccccuagggcauuggugu 122 hsa-miR-326 MIMAT0000756 ccucugggcccuuccuccag 123 hsa-miR-328 MIMAT0000752 cuggcccucucugcccuuccgu 124 hsa-miR-331-5p MIMAT0004700 cuagguauggucccagggaucc 125 hsa-miR-338-3p MIMAT0000763 uccagcaucagugauuuuguug 126 hsa-miR-339-3p MIMAT0004702 ugagcgccucgacgacagagccg 127 hsa-miR-33a* MIMAT0004506 caauguuuccacagugcaucac 128 hsa-miR-33b MIMAT0003301 gugcauugcuguugcauugc 129 hsa-miR-33b* MIMAT0004811 cagugccucggcagugcagccc 130 hsa-miR-342-3p MIMAT0000753 ucucacacagaaaucgcacccgu 131 hsa-miR-34c-3p MIMAT0004677 aaucacuaaccacacggccagg 132 hsa-miR-34c-5p MIMAT0000686 aggcaguguaguuagcugauugc 133 hsa-miR-363* MIMAT0003385 cggguggaucacgaugcaauuu 134 hsa-miR-369-3p MIMAT0000721 aauaauacaugguugaucuuu 135 hsa-miR-370 MIMAT0000722 gccugcugggguggaaccuggu 136 hsa-miR-371-3p MIMAT0000723 aagugccgccaucuuuugagugu 137 hsa-miR-371-5p MIMAT0004687 acucaaacugugggggcacu 138 hsa-miR-375 MIMAT0000728 uuuguucguucggcucgcguga 139 hsa-miR-376b MIMAT0002172 aucauagaggaaaauccauguu 140 hsa-miR-377* MIMAT0000730 aucacacaaaggcaacuuuugu 141 hsa-miR-379 MIMAT0000733 ugguagacuauggaacguagg 142 hsa-miR-382 MIMAT0000737 gaaguuguucgugguggauucg 143 hsa-miR-409-5p MIMAT0001638 agguuacccgagcaacuuugcau 144 hsa-miR-411 MIMAT0003329 uaguagaccguauagcguacg 145 hsa-miR-411* MIMAT0004813 uauguaacacgguccacuaacc 146 hsa-miR-423-5p MIMAT0004748 ugaggggcagagagcgagacuuu 147 hsa-miR-424 MIMAT0001341 cagcagcaauucauguuuugaa 148 hsa-miR-424* MIMAT0004749 caaaacgugaggcgcugcuau 149 hsa-miR-425 MIMAT0003393 aaugacacgaucacucccguuga 150 hsa-miR-429 MIMAT0001536 uaauacugucugguaaaaccgu 151 hsa-miR-448 MIMAT0001532 uugcauauguaggaugucccau 152 hsa-miR-449a MIMAT0001541 uggcaguguauuguuagcuggu 153 hsa-miR-449b MIMAT0003327 aggcaguguauuguuagcuggc 154 hsa-miR-450b-3p MIMAT0004910 uugggaucauuuugcauccaua 155 hsa-miR-451 MIMAT0001631 aaaccguuaccauuacugaguu 156 hsa-miR-452 MIMAT0001635 aacuguuugcagaggaaacuga 157 hsa-miR-454* MIMAT0003884 acccuaucaauauugucucugc 158 hsa-miR-455-3p MIMAT0004784 gcaguccaugggcauauacac 159 hsa-miR-455-5p MIMAT0003150 uaugugccuuuggacuacaucg 160 hsa-miR-483-3p MIMAT0002173 ucacuccucuccucccgucuu 161 hsa-miR-483-5p MIMAT0004761 aagacgggaggaaagaagggag 162 hsa-miR-484 MIMAT0002174 ucaggcucaguccccucccgau 163 hsa-miR-486-3p MIMAT0004762 cggggcagcucaguacaggau 164 hsa-miR-486-5p MIMAT0002177 uccuguacugagcugccccgag 165 hsa-miR-487b MIMAT0003180 aaucguacagggucauccacuu 166 hsa-miR-491-3p MIMAT0004765 cuuaugcaagauucccuucuac 167 hsa-miR-491-5p MIMAT0002807 aguggggaacccuuccaugagg 168 hsa-miR-493 MIMAT0003161 ugaaggucuacugugugccagg 169 hsa-miR-493* MIMAT0002813 uuguacaugguaggcuuucauu 170 hsa-miR-494 MIMAT0002816 ugaaacauacacgggaaaccuc 171 hsa-miR-497 MIMAT0002820 cagcagcacacugugguuugu 172 hsa-miR-498 MIMAT0002824 uuucaagccagggggcguuuuuc 173 hsa-miR-500 MIMAT0004773 uaauccuugcuaccugggugaga 174 hsa-miR-503 MIMAT0002874 uagcagcgggaacaguucugcag 175 hsa-miR-505 MIMAT0002876 cgucaacacuugcugguuuccu 176 hsa-miR-507 MIMAT0002879 uuuugcaccuuuuggagugaa 177 hsa-miR-511 MIMAT0002808 gugucuuuugcucugcaguca 178 hsa-miR-513a-3p MIMAT0004777 uaaauuucaccuuucugagaagg 179 hsa-miR-513a-5p MIMAT0002877 uucacagggaggugucau 180 hsa-miR-513b MIMAT0005788 uucacaaggaggugucauuuau 181 hsa-miR-513c MIMAT0005789 uucucaaggaggugucguuuau 182 hsa-miR-515-5p MIMAT0002826 uucuccaaaagaaagcacuuucug 183 hsa-miR-518b MIMAT0002844 caaagcgcuccccuuuagaggu 184 hsa-miR-518c* MIMAT0002847 ucucuggagggaagcacuuucug 185 hsa-miR-518d-3p MIMAT0002864 caaagcgcuucccuuuggagc 186 hsa-miR-518d-5p MIMAT0005456 cucuagagggaagcacuuucug 187 hsa-miR-518e* MIMAT0005450 cucuagagggaagcgcuuucug 188 hsa-miR-520d-5p MIMAT0002855 cuacaaagggaagcccuuuc 189 hsa-miR-520h MIMAT0002867 acaaagugcuucccuuuagagu 190 hsa-miR-539 MIMAT0003163 ggagaaauuauccuuggugugu 191 hsa-miR-541 MIMAT0004920 uggugggcacagaaucuggacu 192 hsa-miR-545* MIMAT0004785 ucaguaaauguuuauuagauga 193 hsa-miR-548d-3p MIMAT0003323 caaaaaccacaguuucuuuugc 194 hsa-miR-548d-5p MIMAT0004812 aaaaguaauugugguuuuugcc 195 hsa-miR-551a MIMAT0003214 gcgacccacucuugguuucca 196 hsa-miR-551b MIMAT0003233 gcgacccauacuugguuucag 197 hsa-miR-552 MIMAT0003215 aacaggugacugguuagacaa 198 hsa-miR-554 MIMAT0003217 gcuaguccugacucagccagu 199 hsa-miR-556-5p MIMAT0003220 gaugagcucauuguaauaugag 200 hsa-miR-557 MIMAT0003221 guuugcacgggugggccuugucu 201 hsa-miR-559 MIMAT0003223 uaaaguaaauaugcaccaaaa 202 hsa-miR-561 MIMAT0003225 caaaguuuaagauccuugaagu 203 hsa-miR-564 MIMAT0003228 aggcacggugucagcaggc 204 hsa-miR-568 MIMAT0003232 auguauaaauguauacacac 205 hsa-miR-572 MIMAT0003237 guccgcucggcgguggccca 206 hsa-miR-574-5p MIMAT0004795 ugagugugugugugugagugugu 207 hsa-miR-575 MIMAT0003240 gagccaguuggacaggagc 208 hsa-miR-576-3p MIMAT0004796 aagauguggaaaaauuggaauc 209 hsa-miR-578 MIMAT0003243 cuucuugugcucuaggauugu 210 hsa-miR-583 MIMAT0003248 caaagaggaaggucccauuac 211 hsa-miR-586 MIMAT0003252 uaugcauuguauuuuuaggucc 212 hsa-miR-589 MIMAT0004799 ugagaaccacgucugcucugag 213 hsa-miR-589* MIMAT0003256 ucagaacaaaugccgguucccaga 214 hsa-miR-591 MIMAT0003259 agaccauggguucucauugu 215 hsa-miR-595 MIMAT0003263 gaagugugccguggugugucu 216 hsa-miR-601 MIMAT0003269 uggucuaggauuguuggaggag 217 hsa-miR-602 MIMAT0003270 gacacgggcgacagcugcggccc 218 hsa-miR-609 MIMAT0003277 aggguguuucucucaucucu 219 hsa-miR-610 MIMAT0003278 ugagcuaaaugugugcuggga 220 hsa-miR-612 MIMAT0003280 gcugggcagggcuucugagcuccuu 221 hsa-miR-613 MIMAT0003281 aggaauguuccuucuuugcc 222 hsa-miR-614 MIMAT0003282 gaacgccuguucuugccaggugg 223 hsa-miR-615-3p MIMAT0003283 uccgagccugggucucccucuu 224 hsa-miR-616 MIMAT0004805 agucauuggaggguuugagcag 225 hsa-miR-619 MIMAT0003288 gaccuggacauguuugugcccagu 226 hsa-miR-622 MIMAT0003291 acagucugcugagguuggagc 227 hsa-miR-623 MIMAT0003292 aucccuugcaggggcuguugggu 228 hsa-miR-624* MIMAT0003293 uaguaccaguaccuuguguuca 229 hsa-miR-627 MIMAT0003296 gugagucucuaagaaaagagga 230 hsa-miR-630 MIMAT0003299 aguauucuguaccagggaaggu 231 hsa-miR-633 MIMAT0003303 cuaauaguaucuaccacaauaaa 232 hsa-miR-634 MIMAT0003304 aaccagcaccccaacuuuggac 233 hsa-miR-638 MIMAT0003308 agggaucgcgggcggguggcggccu 234 hsa-miR-639 MIMAT0003309 aucgcugcgguugcgagcgcugu 235 hsa-miR-640 MIMAT0003310 augauccaggaaccugccucu 236 hsa-miR-642 MIMAT0003312 gucccucuccaaaugugucuug 237 hsa-miR-644 MIMAT0003314 aguguggcuuucuuagagc 238 hsa-miR-647 MIMAT0003317 guggcugcacucacuuccuuc 239 hsa-miR-648 MIMAT0003318 aagugugcagggcacuggu 240 hsa-miR-652 MIMAT0003322 aauggcgccacuaggguugug 241 hsa-miR-654-5p MIMAT0003330 uggugggccgcagaacaugugc 242 hsa-miR-658 MIMAT0003336 ggcggagggaaguagguccguuggu 243 hsa-miR-659 MIMAT0003337 cuugguucagggagggucccca 244 hsa-miR-662 MIMAT0003325 ucccacguuguggcccagcag 245 hsa-miR-663 MIMAT0003326 aggcggggcgccgcgggaccgc 246 hsa-miR-665 MIMAT0004952 accaggaggcugaggccccu 247 hsa-miR-671-5p MIMAT0003880 aggaagcccuggaggggcuggag 248 hsa-miR-675 MIMAT0004284 uggugcggagagggcccacagug 249 hsa-miR-708 MIMAT0004926 aaggagcuuacaaucuagcuggg 250 hsa-miR-708* MIMAT0004927 caacuagacugugagcuucuag 251 hsa-miR-711 MIMAT0012734 gggacccagggagagacguaag 252 hsa-miR-720 MIMAT0005954 ucucgcuggggccucca 253 hsa-miR-744* MIMAT0004946 cuguugccacuaaccucaaccu 254 hsa-miR-760 MIMAT0004957 cggcucugggucugugggga 255 hsa-miR-765 MIMAT0003945 uggaggagaaggaaggugaug 256 hsa-miR-766 MIMAT0003888 acuccagccccacagccucagc 257 hsa-miR-767-3p MIMAT0003883 ucugcucauaccccaugguuucu 258 hsa-miR-770-5p MIMAT0003948 uccaguaccacgugucagggcca 259 hsa-miR-802 MIMAT0004185 caguaacaaagauucauccuugu 260 hsa-miR-874 MIMAT0004911 cugcccuggcccgagggaccga 261 hsa-miR-876-3p MIMAT0004925 uggugguuuacaaaguaauuca 262 hsa-miR-876-5p MIMAT0004924 uggauuucuuugugaaucacca 263 hsa-miR-877 MIMAT0004949 guagaggagauggcgcaggg 264 hsa-miR-877* MIMAT0004950 uccucuucucccuccucccag 265 hsa-miR-885-3p MIMAT0004948 aggcagcgggguguaguggaua 266 hsa-miR-885-5p MIMAT0004947 uccauuacacuacccugccucu 267 hsa-miR-886-3p MIMAT0004906 cgcgggugcuuacugacccuu 268 hsa-miR-890 MIMAT0004912 uacuuggaaaggcaucaguug 269 hsa-miR-891b MIMAT0004913 ugcaacuuaccugagucauuga 270 hsa-miR-892b MIMAT0004918 cacuggcuccuuucuggguaga 271 hsa-miR-920 MIMAT0004970 ggggagcuguggaagcagua 272 hsa-miR-922 MIMAT0004972 gcagcagagaauaggacuacguc 273 hsa-miR-923 none GUCAGCGGAGGAAAAGAAA 274 CU hsa-miR-92a-2* MIMAT0004508 ggguggggauuuguugcauuac 275 hsa-miR-92b MIMAT0003218 uauugcacucgucccggccucc 276 hsa-miR-92b* MIMAT0004792 agggacgggacgcggugcagug 277 hsa-miR-93 MIMAT0000093 caaagugcuguucgugcagguag 278 hsa-miR-933 MIMAT0004976 ugugcgcagggagaccucuccc 279 hsa-miR-934 MIMAT0004977 ugucuacuacuggagacacugg 280 hsa-miR-935 MIMAT0004978 ccaguuaccgcuuccgcuaccgc 281 hsa-miR-936 MIMAT0004979 acaguagagggaggaaucgcag 282 hsa-miR-937 MIMAT0004980 auccgcgcucugacucucugcc 283 hsa-miR-939 MIMAT0004982 uggggagcugaggcucugggggug 284 hsa-miR-940 MIMAT0004983 aaggcagggcccccgcucccc 285 hsa-miR-96 MIMAT0000095 uuuggcacuagcacauuuuugcu 286 hsa-miR-99a MIMAT0000097 aacccguagauccgaucuugug 287 hsv1-miR-H1 MIMAT0003744 uggaaggacgggaaguggaag 288 hsv1-miR-LAT none uggcggcccggcccggggcc 289 kshv-miR-K12-12 IMAT0003712 accaggccaccauuccucuccg 290 kshv-miR-K12-3 MIMAT0002193 ucacauucugaggacggcagcga 291 kshv-miR-K12-3* MIMAT0002194 ucgcggucacagaaugugaca 292 kshv-miR-K12-4-5p MIMAT0002191 agcuaaaccgcaguacucuagg 293 kshv-miR-K12-6-5p MIMAT0002188 ccagcagcaccuaauccaucgg 294 kshv-miR-K12-8 MIMAT0002186 uaggcgcgacugagagagcacg 295 kshv-miR-K12-9 MIMAT0002185 cuggguauacgcagcugcguaa 296 kshv-miR-K12-9* MIMAT0002184 acccagcugcguaaaccccgcu 297 mghv-miR-M1-6 MIMAT0001569 ugaaacugugugaggugguuuu 298 mghv-miR-M1-9 MIMAT0001573 ucacauuugccuggaccuuuuu 299 mmu-let-7d* MIMAT0000384 cuauacgaccugcugccuuucu 300 mmu-let-7g MIMAT0000121 ugagguaguaguuuguacaguu 301 mmu-miR-298 MIMAT0000376 ggcagaggagggcuguucuuccc 302 mmu-miR-1 MIMAT0000123 uggaauguaaagaaguauguau 303 mmu-miR-101a MIMAT0000133 uacaguacugugauaacugaa 304 mmu-miR-101a* MIMAT0004526 ucaguuaucacagugcugaugc 305 mmu-miR-101b MIMAT0000616 uacaguacugugauagcugaa 306 mmu-miR-122 MIMAT0000246 uggagugugacaaugguguuug 307 mmu-miR-1224 MIMAT0005460 gugaggacuggggagguggag 308 mmu-miR-124 MIMAT0000134 uaaggcacgcggugaaugcc 309 mmu-miR-125a-3p MIMAT0004528 acaggugagguucuugggagcc 310 mmu-miR-125a-5p MIMAT0000135 ucccugagacccuuuaaccuguga 311 mmu-miR-125b-5p MIMAT0000136 ucccugagacccuaacuuguga 312 mmu-miR-126-5p MIMAT0000137 auuauuacuuuugguacgcg 313 mmu-miR-127 MIMAT0000139 ucggauccgucugagcuuggcu 314 mmu-miR-128 MIMAT0000140 ucacagugaaccggucucuuu 315 mmu-miR-129-3p MIMAT0000544 aagcccuuaccccaaaaagcau 316 mmu-miR-130a MIMAT0000141 cagugcaauguuaaaagggcau 317 mmu-miR-133a MIMAT0000145 uuugguccccuucaaccagcug 318 mmu-miR-133b MIMAT0000769 uuugguccccuucaaccagcua 319 mmu-miR-135a* MIMAT0004531 uauagggauuggagccguggcg 320 mmu-miR-136 MIMAT0000148 acuccauuuguuuugaugaugg 321 mmu-miR-138 MIMAT0000150 agcugguguugugaaucaggccg 322 mmu-miR-138* MIMAT0004668 ggcuacuucacaacaccaggg 323 mmu-miR-139-3p MIMAT0004662 uggagacgcggcccuguuggag 324 mmu-miR-140 MIMAT0000151 cagugguuuuacccuaugguag 325 mmu-miR-140* MIMAT0000152 uaccacaggguagaaccacgg 326 mmu-miR-141 MIMAT0000153 uaacacugucugguaaagaugg 327 mmu-miR-142-3p MIMAT0000155 uguaguguuuccuacuuuaugga 328 mmu-miR-143 MIMAT0000247 ugagaugaagcacuguagcuc 329 mmu-miR-146a MIMAT0000158 ugagaacugaauuccauggguu 330 mmu-miR-146b MIMAT0003475 ugagaacugaauuccauaggcu 331 mmu-miR-148b MIMAT0000580 ucagugcaucacagaacuuugu 332 mmu-miR-150 MIMAT0000160 ucucccaacccuuguaccagug 333 mmu-miR-15a* MIMAT0004624 caggccauacugugcugccuca 334 mmu-miR-15b MIMAT0000124 uagcagcacaucaugguuuaca 335 mmu-miR-181b MIMAT0000673 aacauucauugcugucggugggu 336 mmu-miR-181d MIMAT0004324 aacauucauuguugucggugggu 337 mmu-miR-183 MIMAT0000212 uauggcacugguagaauucacu 338 mmu-miR-185 MIMAT0000214 uggagagaaaggcaguuccuga 339 mmu-miR-186 MIMAT0000215 caaagaauucuccuuuugggcu 340 mmu-miR-191* MIMAT0004542 gcugcacuuggauuucguuccc 341 mmu-miR-193 MIMAT0000223 aacuggccuacaaagucccagu 342 mmu-miR-193b MIMAT0004859 aacuggcccacaaagucccgcu 343 mmu-miR-194 MIMAT0000224 uguaacagcaacuccaugugga 344 mmu-miR-199a-5p MIMAT0000229 cccaguguucagacuaccuguuc 345 mmu-miR-199b* MIMAT0000672 cccaguguuuagacuaccuguuc 346 mmu-miR-19a MIMAT0000651 ugugcaaaucuaugcaaaacuga 347 mmu-miR-200a MIMAT0000519 uaacacugucugguaacgaugu 348 mmu-miR-200b MIMAT0000233 uaauacugccugguaaugauga 349 mmu-miR-200b* MIMAT0004545 caucuuacugggcagcauugga 350 mmu-miR-200c MIMAT0000657 uaauacugccggguaaugaugga 351 mmu-miR-202-3p MIMAT0000235 agagguauagcgcaugggaaga 352 mmu-miR-205 MIMAT0000238 uccuucauuccaccggagucug 353 mmu-miR-206 MIMAT0000239 uggaauguaaggaagugugugg 354 mmu-miR-208a MIMAT0000520 auaagacgagcaaaaagcuugu 355 mmu-miR-21 MIMAT0000530 uagcuuaucagacugauguuga 356 mmu-miR-211 MIMAT0000668 uucccuuugucauccuuugccu 357 mmu-miR-22 MIMAT0000531 aagcugccaguugaagaacugu 358 mmu-miR-221 MIMAT0000669 agcuacauugucugcuggguuuc 359 mmu-miR-222 MIMAT0000670 agcuacaucuggcuacugggu 360 mmu-miR-223 MIMAT0000665 ugucaguuugucaaauacccca 361 mmu-miR-23b MIMAT0000125 aucacauugccagggauuacc 362 mmu-miR-26a MIMAT0000533 uucaaguaauccaggauaggcu 363 mmu-miR-27a MIMAT0000537 uucacaguggcuaaguuccgc 364 mmu-miR-27b MIMAT0000126 uucacaguggcuaaguucugc 365 mmu-miR-27b* MIMAT0004522 agagcuuagcugauuggugaac 366 mmu-miR-28* MIMAT0004661 cacuagauugugagcugcugga 367 mmu-miR-290-5p MIMAT0000366 acucaaacuaugggggcacuuu 368 mmu-miR-291a-5p MIMAT0000367 caucaaaguggaggcccucucu 369 mmu-miR-294* MIMAT0004574 acucaaaauggaggcccuaucu 370 mmu-miR-297a MIMAT0000375 auguaugugugcaugugcaugu 371 mmu-miR-299 MIMAT0004577 uaugugggacgguaaaccgcuu 372 mmu-miR-29b MIMAT0000127 uagcaccauuugaaaucaguguu 373 mmu-miR-29c* MIMAT0004632 ugaccgauuucuccugguguuc 374 mmu-miR-301b MIMAT0004186 cagugcaaugguauugucaaagc 375 mmu-miR-302c* MIMAT0003375 gcuuuaacaugggguuaccugc 376 mmu-miR-30a MIMAT0000128 uguaaacauccucgacuggaag 377 mmu-miR-30c MIMAT0000514 uguaaacauccuacacucucagc 378 mmu-miR-30c-1* MIMAT0004616 cugggagaggguuguuuacucc 379 mmu-miR-30e MIMAT0000248 uguaaacauccuugacuggaag 380 mmu-miR-31 MIMAT0000538 aggcaagaugcuggcauagcug 381 mmu-miR-320 MIMAT0000666 aaaagcuggguugagagggcga 382 mmu-miR-322 MIMAT0000548 cagcagcaauucauguuuugga 383 mmu-miR-323-3p MIMAT0000551 cacauuacacggucgaccucu 384 mmu-miR-324-3p MIMAT0000556 ccacugccccaggugcugcu 385 mmu-miR-324-5p MIMAT0000555 cgcauccccuagggcauuggugu 386 mmu-miR-326 MIMAT0000559 ccucugggcccuuccuccagu 387 mmu-miR-327 MIMAT0004867 acuugaggggcaugaggau 388 mmu-miR-328 MIMAT0000565 cuggcccucucugcccuuccgu 389 mmu-miR-331-5p MIMAT0004643 cuagguauggucccagggaucc 390 mmu-miR-339-3p MIMAT0004649 ugagcgccucggcgacagagccg 391 mmu-miR-341 MIMAT0000588 ucggucgaucggucggucggu 392 mmu-miR-342-3p MIMAT0000590 ucucacacagaaaucgcacccgu 393 mmu-miR-34b-5p MIMAT0000382 aggcaguguaauuagcugauugu 394 mmu-miR-34c* MIMAT0004580 aaucacuaaccacacagccagg 395 mmu-miR-369-3p MIMAT0003186 aauaauacaugguugaucuuu 396 mmu-miR-370 MIMAT0001095 gccugcugggguggaaccuggu 397 mmu-miR-374 MIMAT0003727 auauaauacaaccugcuaagug 398 mmu-miR-375 MIMAT0000739 uuuguucguucggcucgcguga 399 mmu-miR-376b MIMAT0001092 aucauagaggaacauccacuu 400 mmu-miR-379 MIMAT0000743 ugguagacuauggaacguagg 401 mmu-miR-380-3p MIMAT0000745 uauguaguaugguccacaucuu 402 mmu-miR-382 MIMAT0000747 gaaguuguucgugguggauucg 403 mmu-miR-384-5p MIMAT0004745 uguaaacaauuccuaggcaaugu 404 mmu-miR-409-5p MIMAT0004746 agguuacccgagcaacuuugcau 405 mmu-miR-411 MIMAT0004747 uaguagaccguauagcguacg 406 mmu-miR-411* MIMAT0001093 uauguaacacgguccacuaacc 407 mmu-miR-423-5p MIMAT0004825 ugaggggcagagagcgagacuuu 408 mmu-miR-425 MIMAT0004750 aaugacacgaucacucccguuga 409 mmu-miR-429 MIMAT0001537 uaauacugucugguaaugccgu 410 mmu-miR-434-5p MIMAT0001421 gcucgacucaugguuugaacca 411 mmu-miR-450b-3p MIMAT0003512 auugggaacauuuugcaugcau 412 mmu-miR-451 MIMAT0001632 aaaccguuaccauuacugaguu 413 mmu-miR-455 MIMAT0003742 gcaguccacgggcauauacac 414 mmu-miR-465c-3p MIMAT0004874 gaucagggccuuucuaaguaga 415 mmu-miR-466d-5p MIMAT0004930 ugugugugcguacauguacaug 416 mmu-miR-466f-3p MIMAT0004882 cauacacacacacauacacac 417 mmu-miR-467e* MIMAT0005294 auauacauacacacaccuauau 418 mmu-miR-483 MIMAT0004782 aagacgggagaagagaagggag 419 mmu-miR-484 MIMAT0003127 ucaggcucaguccccucccgau 420 mmu-miR-486 MIMAT0003130 uccuguacugagcugccccgag 421 mmu-miR-487b MIMAT0003184 aaucguacagggucauccacuu 422 mmu-miR-494 MIMAT0003182 ugaaacauacacgggaaaccuc 423 mmu-miR-497 MIMAT0003453 cagcagcacacugugguuugua 424 mmu-miR-505 IMAT0003513 cgucaacacuugcugguuuucu 425 mmu-miR-511 MIMAT0004940 augccuuuugcucugcacuca 426 mmu-miR-539 MIMAT0003169 ggagaaauuauccuuggugugu 427 mmu-miR-540-3p MIMAT0004786 caagggucacccucugacucugu 428 mmu-miR-551b MIMAT0003890 gcgacccauacuugguuucag 429 mmu-miR-568 MIMAT0004892 auguauaaauguauacacac 430 mmu-miR-574-5p MIMAT0004893 ugagugugugugugugagugugu 431 mmu-miR-652 MIMAT0003711 aauggcgccacuaggguugug 432 mmu-miR-654-5p MIMAT0004897 ugguaagcugcagaacaugugu 433 mmu-miR-669a MIMAT0003477 aguugugugugcauguucaugu 434 mmu-miR-671-5p MIMAT0003731 aggaagcccuggaggggcuggag 435 mmu-miR-685 MIMAT0003463 ucaauggcugaggugaggcac 436 mmu-miR-686 MIMAT0003464 auugcuucccagacggugaaga 437 mmu-miR-688 MIMAT0003467 ucgcaggcgacuacuuauuc 438 mmu-miR-701 MIMAT0003491 uuagccgcugaaauagaugga 439 mmu-miR-706 MIMAT0003496 agagaaacccugucucaaaaaa 440 mmu-miR-708 MIMAT0004828 aaggagcuuacaaucuagcuggg 441 mmu-miR-710 MIMAT0003500 ccaagucuuggggagaguugag 442 mmu-miR-711 MIMAT0003501 gggacccggggagagauguaag 443 mmu-miR-712 MIMAT0003502 cuccuucacccgggcgguacc 444 mmu-miR-714 MIMAT0003505 cgacgagggccggucggucgc 445 mmu-miR-720 MIMAT0003484 aucucgcuggggccucca 446 mmu-miR-721 MIMAT0003515 cagugcaauuaaaagggggaa 447 mmu-miR-744* MIMAT0004820 cuguugccacuaaccucaaccu 448 mmu-miR-760 MIMAT0003898 cggcucugggucugugggga 449 mmu-miR-770-5p MIMAT0004822 agcaccacgugucugggccacg 450 mmu-miR-7a MIMAT0000677 uggaagacuagugauuuuguugu 451 mmu-miR-7b MIMAT0000678 uggaagacuugugauuuuguugu 452 mmu-miR-877 MIMAT0004861 guagaggagauggcgcaggg 453 mmu-miR-877* MIMAT0004862 uguccucuucucccuccuccca 454 mmu-miR-881* MIMAT0004845 cagagagauaacagucacaucu 455 mmu-miR-882 MIMAT0004847 aggagagaguuagcgcauuagu 456 mmu-miR-93 MIMAT0000540 caaagugcuguucgugcagguag 457 mmu-miR-96 MIMAT0000541 uuuggcacuagcacauuuuugcu 458 mmu-miR-99a MIMAT0000131 aacccguagauccgaucuugug 459

The devices and methods of the present disclosure have been described with reference to exemplary embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the exemplary embodiments be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof. 

What is claimed is:
 1. A method of detecting or predicting a neurological condition, comprising: generating a first microRNA profile from a biological sample, wherein the first microRNA profile comprises the amount of at least one specific microRNA sequence, by: receiving the biological sample, the sample being a tissue, serum, or plasma; and isolating microRNA from the biological sample by extracting the microRNA with an organic solvent and obtaining an aqueous phase containing the microRNA, then purifying the aqueous phase through a silica membrane to isolate the microRNA; using hybridization to identify the microRNA sequences; and measuring the amount of at least one specific microRNA sequence; and comparing the amount of the at least one specific microRNA sequence to a reference to provide information for detecting or predicting the neurological condition.
 2. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of let-7g, miR-298, miR-1, miR-101a*, miR-101b, miR-1224, miR-126-5p, miR-127, miR-128, miR-129-3p, miR-133b, miR-136, miR-138, miR-138*, miR-139-3p, miR-140, miR-140*, miR-142-3p, miR-143, miR-146a, miR-146b, miR-148b, miR-150, miR-15a*, miR-15b, miR-181b, miR-181d, miR-183, miR-185, miR-186, miR-191*, miR-194, miR-19a, miR-200a, miR-200b, miR-200b*, miR-202-3p, miR-206, miR-208a, miR-21, miR-211, miR-221, miR-222, miR-223, miR-27a, miR-27b*, miR-28*, miR-290-5p, miR-291a-5p, miR-297a, miR-299, miR-29b, miR-29c*, miR-301b, miR-302c*, miR-30c, miR-31, miR-322, miR-323-3p, miR-324-3p, miR-324-5p, miR-326, miR-328, miR-331-5p, miR-341, miR-34b-5p, miR-34c*, miR-369-3p, miR-374, miR-376b, miR-379, miR-380-3p, miR-382, miR-384-5p, miR-409-5p, miR-411, miR-411*, miR-423-5p, miR-425, miR-429, miR-434-5p, miR-450b-3p, miR-451, miR-455, miR-465c-3p, miR-466d-5p, miR-467e*, miR-484, miR-486, miR-487b, miR-497, miR-505, miR-511, miR-539, miR-540-3p, miR-551b, miR-568, miR-654-5p, miR-669a, miR-686, miR-688, miR-699, miR-701, miR-706, miR-708, miR-720, miR-721, miR-744*, miR-760, miR-770-5p, miR-7a, miR-7b, miR-881*, miR-93, miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
 3. The method of claim 2, wherein the biological sample is serum or plasma; and further comprising generating a second microRNA profile from cerebrospinal fluid, wherein the second microRNA profile comprises the amount of at least one specific microRNA sequence selected from the group consisting of miR-134, miR-151-5p, miR-223*, miR-302d, miR-325, miR-335*, miR-377*, miR-483-5p, miR-505, miR-509-5p, miR-515-3p, miR-518e, miR-556-3p, miR-589, miR-616*, miR-652, miR-767-3p, miR-873, miR-892a, miR-923, and human orthologs thereof.
 4. The method of claim 3, wherein at least two differentially expressed microRNA sequences are identified in the first microRNA profile and at least two differentially expressed microRNA sequences are identified in the second microRNA profile.
 5. The method of claim 4, wherein the at least two differentially expressed microRNA sequences in the first microRNA profile are different from the at least two differentially expressed microRNA sequences in the second microRNA profile.
 6. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of miR-122, miR-124, miR-124a, miR-124b, miR-128a, miR-128b, miR-129, miR-132, miR-134, miR-137, miR-138, miR-149, miR-153, miR-154*, miR-181a, miR-181b, miR-181bN, miR-181c, miR-181d, miR-184, miR-204, miR-213, miR-219, miR-323, miR-324-5p, miR-328, miR-330, miR-338, miR-340, miR-342, miR-348, miR-370, miR-379, miR-383, miR-432, miR-433, miR-485-3p, miR-485-5p, miR-491, miR-9, miR-9*, and human orthologs thereof.
 7. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of miR-134, miR-151-5p, miR-223*, miR-302d, miR-325, miR-335*, miR-377*, miR-483-5p, miR-505, miR-509-5p, miR-515-3p, miR-518e, miR-556-3p, miR-589, miR-616*, miR-652, miR-767-3p, miR-873, miR-892a, miR-923, and human orthologs thereof.
 8. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of let-7g, miR-298, miR-1, miR-101a*, miR-101b, miR-1224, miR-126-5p, miR-127, miR-128, miR-129-3p, miR-133b, miR-136, miR-138, miR-138*, miR-139-3p, miR-140, miR-140*, miR-142-3p, miR-143, miR-146a, miR-146b, miR-148b, miR-150, miR-15a*, miR-15b, miR-181b, miR-181d, miR-183, miR-185, miR-186, miR-191*, miR-194, miR-19a, miR-200a, miR-200b, miR-200b*, miR-202-3p, miR-206, miR-208a, miR-21, miR-211, miR-221, miR-222, miR-223, miR-27a, miR-27b*, miR-28*, miR-290-5p, miR-291a-5p, miR-297a, miR-299, miR-29b, miR-29c*, miR-301b, miR-302c*, miR-30c, miR-31, miR-322, miR-323-3p, miR-324-3p, miR-324-5p, miR-326, miR-328, miR-331-5p, miR-341, miR-34b-5p, miR-34c*, miR-369-3p, miR-374, miR-376b, miR-379, miR-380-3p, miR-382, miR-384-5p, miR-409-5p, miR-411, miR-411*, miR-423-5p, miR-425, miR-429, miR-434-5p, miR-450b-3p, miR-451, miR-455, miR-465c-3p, miR-466d-5p, miR-467e*, miR-484, miR-486, miR-487b, miR-497, miR-505, miR-511, miR-539, miR-540-3p, miR-551b, miR-568, miR-654-5p, miR-669a, miR-686, miR-688, miR-699, miR-701, miR-706, miR-708, miR-720, miR-721, miR-744*, miR-760, miR-770-5p, miR-7a, miR-7b, miR-881*, miR-93, miR-96, mghv-miR-M1-6, mghv-miR-M1-9; miR-122, miR-124, miR-124a, miR-124b, miR-128a, miR-128b, miR-129, miR-132, miR-134, miR-137, miR-138, miR-149, miR-153, miR-154*, miR-181a, miR-181b, miR-181bN, miR-181c, miR-181d, miR-184, miR-204, miR-213, miR-219, miR-323, miR-324-5p, miR-328, miR-330, miR-338, miR-340, miR-342, miR-348, miR-370, miR-379, miR-383, miR-432, miR-433, miR-485-3p, miR-485-5p, miR-491, miR-9, miR-9*; miR-134, miR-151-5p, miR-223*, miR-302d, miR-325, miR-335*, miR-377*, miR-483-5p, miR-505, miR-509-5p, miR-515-3p, miR-518e, miR-556-3p, miR-589, miR-616*, miR-652, miR-767-3p, miR-873, miR-892a, miR-923; and human orthologs thereof.
 9. The method of claim 1, wherein the at least one specific microRNA sequence is a plurality of microRNA sequences.
 10. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of let-7g, miR-298, miR-1, miR-101a*, miR-101b, miR-1224, miR-126-5p, miR-127, miR-128, miR-129-3p, miR-133b, miR-136, miR-138, miR-138*, miR-139-3p, miR-140, miR-140*, miR-142-3p, miR-143, miR-146a, miR-146b, miR-148b, miR-150, miR-15a*, miR-15b, miR-181b, miR-181d, miR-183, miR-185, miR-186, miR-191*, miR-194, miR-19a, miR-200a, miR-200b, miR-200b*, miR-202-3p, miR-206, miR-208a, miR-21, miR-211, miR-221, miR-222, miR-223, miR-27a, miR-27b*, miR-28*, miR-290-5p, miR-291a-5p, miR-297a, miR-299, miR-29b, miR-29c*, miR-301b, miR-302c*, miR-30c, miR-31, miR-322, miR-323-3p, miR-324-3p, miR-324-5p, miR-326, miR-328, miR-331-5p, miR-341, miR-34b-5p, miR-34c*, miR-369-3p, miR-374, miR-376b, miR-379, miR-380-3p, miR-382, miR-384-5p, miR-409-5p, miR-411, miR-411*, miR-423-5p, miR-425, miR-429, miR-434-5p, miR-450b-3p, miR-451, miR-455, miR-465c-3p, miR-466d-5p, miR-467e*, miR-484, miR-486, miR-487b, miR-497, miR-505, miR-511, miR-539, miR-540-3p, miR-551b, miR-568, miR-654-5p, miR-669a, miR-686, miR-688, miR-699, miR-701, miR-706, miR-708, miR-720, miR-721, miR-744*, miR-760, miR-770-5p, miR-7a, miR-7b, miR-881*, miR-93, miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
 11. The method of claim 1, wherein the at least one specific microRNA sequence is selected from the group consisting of let-7g, miR-298, miR-101a*, miR-101b, miR-1224, miR-126-5p, miR-128, miR-129-3p, miR-133b, miR-138*, miR-139-3p, miR-140*, miR-146a, miR-148b, miR-15a*, miR-15b, miR-181b, miR-181d, miR-185, miR-186, miR-191*, miR-19a, miR-200b*, miR-202-3p, miR-208a, miR-211, miR-27b*, miR-28*, miR-290-5p, miR-291a-5p, miR-297a, miR-299, miR-29c*, miR-301b, miR-302c*, miR-322, miR-323-3p, miR-324-3p, miR-324-5p, miR-326, miR-328, miR-331-5p, miR-341, miR-34b-5p, miR-34c*, miR-369-3p, miR-374, miR-376b, miR-379, miR-380-3p, miR-382, miR-384-5p, miR-409-5p, miR-411, miR-411*, miR-423-5p, miR-425, miR-429, miR-434-5p, miR-450b-3p, miR-465c-3p, miR-466d-5p, miR-467e*, miR-505, miR-511, miR-539, miR-540-3p, miR-551b, miR-568, miR-654-5p, miR-669a, miR-686, miR-688, miR-699, miR-701, miR-706, miR-720, miR-721, miR-744*, miR-760, miR-770-5p, miR-7a, miR-7b, miR-881*, miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
 12. A method of using microRNA to monitor a neurological condition, comprising: generating a first microRNA profile from a first biological sample of a patient; administering a treatment to the patient; generating a second microRNA profile from a second biological sample of the patient; comparing the second microRNA profile with the first microRNA profile to identify differentially expressed microRNA sequences; and identifying a change in the neurological condition based on the identity or the amounts of the differentially expressed microRNA sequences. 